DESIGN SCENARIOS METHODOLOGY – ENABLING REQUIREMENTS-DRIVEN DESIGN SPACES
A DISSERTATION
SUBMITTED TO THE DEPARTMENT OF
CIVIL AND ENVIRONMENTAL ENGINEERING
AND THE COMMITTEE ON GRADUATE STUDIES
OF STANFORD UNIVERSITY
IN PARTIAL FULFILLMENT OF THE REQUIREMENTS
FOR THE DEGREE OF
DOCTOR OF PHILOSOPHY
Victor Gane
May 2011
© 2011 by Victor Gane. All Rights Reserved. Re-distributed by Stanford University under license with the author.
This work is licensed under a Creative Commons Attribution- Noncommercial 3.0 United States License. http://creativecommons.org/licenses/by-nc/3.0/us/
This dissertation is online at: http://purl.stanford.edu/qs170jk0633
ii I certify that I have read this dissertation and that, in my opinion, it is fully adequate in scope and quality as a dissertation for the degree of Doctor of Philosophy.
Martin Fischer, Primary Adviser
I certify that I have read this dissertation and that, in my opinion, it is fully adequate in scope and quality as a dissertation for the degree of Doctor of Philosophy.
John Haymaker, Co-Adviser
I certify that I have read this dissertation and that, in my opinion, it is fully adequate in scope and quality as a dissertation for the degree of Doctor of Philosophy.
Mark Cutkosky
I certify that I have read this dissertation and that, in my opinion, it is fully adequate in scope and quality as a dissertation for the degree of Doctor of Philosophy.
Vladimir Bazjanac
Approved for the Stanford University Committee on Graduate Studies. Patricia J. Gumport, Vice Provost Graduate Education
This signature page was generated electronically upon submission of this dissertation in electronic format. An original signed hard copy of the signature page is on file in University Archives.
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Abstract
During the conceptual design process, the building shape, orientation, materials and other major properties are established, all of which have a substantial impact on multi- aspect performance. In this process, multidisciplinary teams define project objectives, create various alternatives, and try to understand their impacts and value. With non- parametric Computer Aided Design (CAD) methods designers produce and analyze as few as three alternatives, whereas with parametric CAD – they can generate thousands. However, with current parametric methods, CAD experts lack a comprehensive method to build and analyze multi-objective parametric models. Therefore the resulting models do not effectively encapsulate multi-objective value measures.
This research introduces the Design Scenarios Methodology (DS), which builds on research from Systems Engineering, Process Modeling, and Parametric Modeling. With DS, Enablers use Methods to create Elements using five interconnected models to define (1) project stakeholders and their objectives, (2) designer logic used to address objectives, (3) the connection between designer logic and computable models to generate alternatives, (4) the predicted impact and (5) value of the generated alternatives. I implemented DS as a web-based software prototype and tested it on an industry project. The results provide evidence that the DS method provides CAD experts with well-defined logic and parameters for addressing objectives and the process enables creating parametric alternatives with clear multi-objective values that potentially provide clients with better building designs.
This thesis lays the foundation for future research on automating the design alternative generation and analyses processes by leveraging such well-established methods as Multi-Disciplinary Optimization.
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Acknowledgments
The last few years have been an extraordinary journey of learning, discovery, collaboration, friendship, frustration, and joy. Having completed my Ph.D. is perhaps my most fundamental achievement to date. I came out of it a more seasoned and ever more curious thinker, yearning to continue on the path of challenging myself and contributing to the advancement of the science. I attribute a great part of this success to many great individuals, to whom I express my gratitude.
I would like to first of all thank John Haymaker, my advisor. John has been instrumental in inspiring me to pursue a Ph.D. He is a true friend, who has always helped guide and support my research. No matter whether I was working locally on my papers, or remotely on industry case studies, John always asked hard questions, gave great feedback, and encouraged my intuition for developing prototype solutions for my research problem.
I’d like to express my gratitude to my co-advisor, Martin Fischer, whose drive for excellence was an important inspiration over the years. Martin helped focus the contribution of my research and sharpen the story. His comments to my papers, as well as feedback to numerous informal and formal presentations were invaluable.
I am grateful to my reading committee members Vlado Bazjanac, Mark Cutkosky, and Axel Kilian. I met Vlado during one of his visits to Stanford and immediately grew to respect and appreciate his holistic view of the building design process. Professor Cutkosky’s wealth of knowledge from other design disciplines such as mechanical engineering and robotics offered an important alternative perspective to my research. Axel Kilian from Princeton University was an early motivation to this research when he taught a workshop on computational design methods during my graduate studies at the Massachusetts Institute of Technology when I became interested in applying parametric Computer Aided Design in the conceptual design of buildings. Axel is a true friend and his humility and profound knowledge is inspiring.
Many others helped shape this experience. I thank the Precourt Center for Energy Efficiency for partially funding my research. David Anderson is the software architect
v Victor Gane that helped implement my ideas into a testable software prototype. John Kunz, Renate Fruchter, Ray Levitt asked fundamental questions and reinforced the rigorous scientific thinking that a Ph.D. process entails. I thank the CIFE faculty for introducing me early on to the “Horseshoe” concept of structuring my Ph.D. research, without which it might have been much harder to stay focused and understand where in the overall research process I stood.
I thank Ross Wimer, Bill Baker, Mark Sarkisian, Bernie Gandras, Luke Leung, and Eric Zachrison from Skidmore, Owings and Merrill who were instrumental in enabling me to test my research on industry projects and learn firsthand the anticipated practical impact.
Finally, I am grateful to my many friends and peers at Stanford who throughout the years contributed to making this a great journey: Reid Senescu, Ben Welle, Rene Morcos, Tobias Maile, Ben Suter, Caroline Clevenger, Wendy Li, Akiko Yamada.
And last but not least, I dedicate this dissertation to my parents, my most ardent supporters. Without their unconditional belief in my abilities, encouragement, and support of higher scholarly pursuits, it would be hard to imagine I would be where I am today.
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Table of Contents
Abstract ...... iv
Acknowledgments ...... v
Table of Contents ...... vii
List of Tables ...... x
List of Illustrations ...... xi
Chapter 1: Introduction ...... 1 1 Observed problem ...... 2 2 Research questions ...... 5 3 Points of departure ...... 5 4 Research method / tasks ...... 6 5 Research results / validation ...... 10 6 Contributions to knowledge ...... 11 7 Predicted practical impact ...... 12 8 Conclusion ...... 13 9 References ...... 13
Chapter 2: Benchmarking current conceptual design processes ...... 16 1 Abstract ...... 16 2 Introduction: Need for Effective Conceptual High-Rise Design Processes ...... 16 2.1 Points of departure: What are current high-rise design processes and how do we document and measure them? ...... 18 2.1.1 Design theory ...... 18 2.1.2 Process modeling ...... 19 2.1.3 High-rise classification and key design criteria ...... 20 3 Methods: Documenting and Analyzing Current Practice ...... 27 3.1 Documenting Current Conceptual Design Process ...... 27 3.2 A survey of industry professionals on current conceptual design process ...... 35 3.2.1 Organizations ...... 36 3.2.2 Options ...... 38 3.2.3 Analyses ...... 39
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3.2.4 Decisions ...... 40 3.2.5 Time investment ...... 41 4 Conclusion: Potential cost of underperforming conceptual design processes ...... 43 4.1 Organizations...... 44 4.2 Goals ...... 44 4.3 Options ...... 45 4.4 Analyses ...... 45 4.5 Decisions ...... 46 5 Bibliography ...... 47
Chapter 3: Design Scenarios – enabling requirements-driven parametric design spaces ...... 52 1 Abstract ...... 52 2 Introduction: the need for effective conceptual design processes ...... 53 3 Framework for measuring the design space quality ...... 57 4 Points of Departure: design space exploration methods ...... 58 4.1 Concurrent Engineering – integrate and parallelize tasks ...... 59 4.2 Quality Function Deployment – translate user needs into design characteristics .... 60 4.3 Requirements Engineering – determine and manage requirements ...... 61 4.4 Axiomatic Design – generate requirements and enable parameters ...... 62 4.5 Process modeling – represent and measure design spaces ...... 63 4.6 Parametric modeling – generate alternative spaces ...... 64 5 Design Scenarios - methodology description ...... 66 5.1 Requirements Model (RM) ...... 69 5.2 Scenarios Model (SM) ...... 71 5.3 Parametric Process Model (PPM) ...... 75 5.4 Alternatives Analysis Model (AAM) ...... 80 5.5 Illustrative Example ...... 82 5.5.1 Requirements Model ...... 83 5.5.2 Scenarios Model ...... 84 5.5.3 Parametric Process Model ...... 87 5.5.4 Alternatives Analyses Model ...... 90 5.5.5 Testing the practical value of DS ...... 93
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6 Conclusion ...... 94 7 Bibliography ...... 96
Chapter 4: Application of Design Scenarios methodology to evaluate the effectiveness of transparent parametric CAD ...... 103 1 Abstract ...... 103 2 Introduction – the need for effective conceptual design processes ...... 103 3 Conceptual design process using parametric modeling with no formal implementation method...... 108 3.1 Tower 1 test case ...... 108 3.2 Tower 2 test case ...... 114 3.3 Summary ...... 117 4 Conceptual design process using Design Scenarios (DS) to clarify design spaces . 118 4.1 Summary of Tower 3 design team process ...... 119 4.2 Requirements Model (RM) ...... 120 4.3 Scenarios Model (SM) ...... 122 4.4 Parametric Process Model (PPM) ...... 126 4.5 Alternatives Analysis Model (AAM) ...... 129 5 Conclusions and future opportunities ...... 134 6 Bibliography ...... 138 7 Appendix ...... 141
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List of Tables
Table 3-1: Summary of topics covered by each guideline ...... 57 Table 3-2: RM graphical notation, definitions, and data schema in the Design Scenarios software prototype ...... 70 Table 3-3: SM graphical notation, definitions, and data schema and in the Design Scenarios software ...... 73 Table 3-4: Table 4. PPM ontology and graphical notation ...... 77 Table 3-5: AAM ontology and graphical notation ...... 81 Table 3-6: Three geometric alternatives selected for further analysis and the input and resulting output parameters ...... 89 Table 4-1: Tower 1 project facts and requirements. No formal method to gather and prioritize requirements was implemented ...... 109 Table 4-2: Input parameters and constrained ranges ...... 114 Table 4-3: Selection of Tower 2 project facts and design requirements ...... 115 Table 4-4: Input parameters and constrained ranges describing the model in Figure 4-6a...... 117 Table 4-5: Project facts ...... 119 Table 4-6: Input parameters and constrained ranges ...... 126 Table 4-7: 10 design alternatives and a selection of input parameters used to generate each alternative ...... 129 Table 4-8: A comparison of four data sets quantifying conceptual design process performance. Items in bold denote significant improvements over current practice...... 135
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List of Illustrations
Figure 1-1: CIFE “horseshoe” research method ...... 2 Figure 1-2: Theory defines design space in terms of four subspaces with elements and relationships among them ...... 2 Figure 1-3: a) The Alternative Space consists of scenarios, alternatives, and options; b) example of impact of alternatives given the goals and constraints ...... 3 Figure 1-4: Design Theory and Systems Engineering say that design spaces need to be both well-defined and comprehensive ...... 4 Figure 1-5: Selection of theoretical points of departure used in this dissertation ...... 6 Figure 1-6: Design Scenarios Methodology schema and connections among four spaces ...... 7 Figure 1-7: Objective space enablers, methods, and elements ...... 8 Figure 1-8: Alternative space logic enablers, methods, and elements ...... 8 Figure 1-9: Alternative space geometry enablers, methods, and elements ...... 9 Figure 1-10: Impact space enablers, methods, and elements ...... 10 Figure 1-11: Value space enablers, methods, and elements ...... 10 Figure 1-12: a) Tyrol Tower, Worgl, Austria; b) Infinity Tower, Dubai, UAE; c) Transbay Tower, San Francisco; d) Jeddah Towers, Jeddah, Saudi Arabia. Images courtesy of SOM...... 11 Figure 1-13: Quotes collected from DS validation on industry projects ...... 13 Figure 2-1: Typical stakeholders and design team organizational hierarchy ...... 22 Figure 2-2: Process model describing the case study conceptual design. Nodes A & B describe the early stages, in which decisions about team size, composition, duration, and deliverables were made by the design firm owner, project manager, and senior designer. Nodes C, D, & E describe the process by which the design team evaluated the project context and requirements and proposed the first design concepts in a design charrette. Nodes F, G, H, & I describe the process by which the design team developed 2D working drawings to calculate whether area and building efficiency requirements were met and a 3D model for visual evaluation of the design. Designers repeated the process several times before these
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requirements were met. Nodes J, K, L, M & N show that only after the senior designer accepted a design option for final development were the mechanical and structural engineers involved, the geometry for physical model prototyping prepared, and the conceptual design package assembled. Mechanical and structural engineers rationalized the design rather than participated from the beginning in decision making. Adapted from SOM case study project ...... 28 Figure 2-3: Tyrol Tower site, Worgl, Austria. With permission from SOM ...... 29 Figure 2-4: a) Tyrol Tower concept sketches – skiing was the prevailing design theme; the first concept was influenced by the shape of the slalom ski poll, the second abstracted a skier in motion with a 2.5 degree inclination; b) massing, orientation, and tower positioning strategy were influenced by the site geometry, prevailing wind direction, and the need to link the tower to the adjacent site. With permission from SOM ...... 30 Figure 2-5: a) Tyrol Tower typical and unique floor plans; b) building section. With permission from SOM ...... 31 Figure 2-6: a) Tyrol Tower conceptual renderings; b) physical model. With permission from SOM ...... 32 Figure 2-7: A late concept sketch proposed by the senior designer in response to the previously unarticulated goal of providing the building with natural ventilation. Implementing the proposed side scallops would have invalidated most of the completed work, and because the time constraints the idea was abandoned. With permission from SOM ...... 33 Figure 2-8: Information produced by mechanical engineers did not include any model- based analyses but rather a set of conceptual, untested recommendations, such as: a) stack effect diagram based on architect’s section; b) standardized graphs of annual wet and dry bulb temperatures, wind rose, and annual direct solar radiation to inform which months of the year were most suitable for natural ventilation. With permission from SOM ...... 34 Figure 2-9: Process performance metrics adapted from the SOM case study project .. 35
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Figure 2-10: Average reported survey results of team size and composition. A typical design team includes 12 professionals, 65% of whom are architects. Most design and engineering positions were generally reported to be filled by a single professional with the exception of intern (3) and mid-level architects (2) ...... 36 Figure 2-11: Average reported survey results (% respondents) showing: a) the percentage of projects that employed each method of goals communication– goals on most projects were communicated verbally; b) the clarity of the identified goals – most had low to low / medium clarity (Note: respondents were allowed to choose multiple answers) ...... 37 Figure 2-12: Average reported survey results (% respondents) showing: a) who established project goals – mostly clients and architects with little or no participation from engineers. b) major project constraints – most were determined by the developer emphasizing commercial efficiency as opposed to lifecycle efficiency...... 38 Figure 2-13: Average reported survey results (% of respondents) showing: a) the number of design options generated during conceptual design – majority indicated three; b) tools used are traditional CAD or graphics software that support generating single, static solutions. Very few respondents used emerging technologies, such as parametric modeling or energy analysis tools. (Note: For question (b) the respondents were allowed to choose multiple answers) ...... 39 Figure 2-14: Average reported survey results (% respondents) showing the model- based analyses performed during conceptual design. The performed analyses address predominantly architectural concerns (i.e., budget constraints, program requirements). (Note: respondents were allowed to choose multiple answers) ... 40 Figure 2-15: Average reported survey results showing which objectives designers consider when making design decisions. Architectural criteria (i.e., aesthetics, area efficiency, site views) prevail over engineering performance criteria (i.e., energy efficiency, natural ventilation, structural performance) ...... 41 Figure 2-16: Average reported survey results (% respondents) showing conceptual design duration, which predominantly fluctuates between 4 - 6 weeks ...... 41
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Figure 2-17: Average reported survey results showing: a) total man hours invested by each team member type during concept design phase; b) total number of hours by discipline ...... 42 Figure 2-18: Average reported survey results showing percentages of the total time spent on goal definition, option generation, analysis, and preparation of presentation materials ...... 42 Figure 2-19: Averaged survey concept design process performance metrics ...... 43 Figure 3-1: Example of AEC scenarios – concept sketches of a high-rise located in the Austrian Alps, which are derived from a ski pole (left) and a ski boot (right) (Gane and Haymaker, 2010) ...... 54 Figure 3-2: Summary of the concepts used to describe a design space and search methods ...... 56 Figure 3-3: Summary of how PODs satisfy the identified needs. This paper focuses on creating an ontology for building multidisciplinary AEC alternative spaces and a method to translate design requirements from the objective space to the alternative, impact, and value spaces ...... 66 Figure 3-4: Design Scenarios methodology process description. The Objective Space is captured in the Requirements Model; the Logical Alternative Space in the Scenarios Model; the Geometric Alternative Space in the Parametric Process Model; the Impact and Value Spaces in the Alternatives Analysis Model ...... 68 Figure 3-5: First Order logic implemented in the SM. R1, R2 are the requirement nodes generated in the SM; A, B, C, D represent the action, strategy, or parameter nodes in SM ...... 72 Figure 3-6: The site for a teaching space ...... 82 Figure 3-7: The Requirements Model captures the stakeholders’ constraints, goals, and preferences for goals. Stakeholders distribute a percentage of preference (totaling 100%) to each identified goal ...... 83 Figure 3-8: The architect suggests two scenarios (square and rectangular classroom) that enables determine the desired range of geometric variations ...... 84
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Figure 3-9: Scenarios Model for the University Quad illustrative example. The model starts with the two Constraints and two Goals transferred from the RM and design stakeholders rationalize them into Actions, Strategies, Parameters and Parametric Constraints. AND, OR, XOR logical gateways are used to describe relations between Actions, Strategies, and Parameters ...... 86 Figure 3-10: Component-level PPM illustrates the CAD model decomposition into six components shown in hierarchical order ...... 87 Figure 3-11: Schema-level PPM describes the composition of: a) “Property outline” component; b)”Building footprint” component ...... 88 Figure 3-12: Final composite geometry-level PPM. Note that the nodes’ attributes are toggled off to help simplify the model. The model helps understand which nodes are affected when parameter values are changed by highlighting them (i.e., the Building width value was changed from 40’ to 70’) ...... 89 Figure 3-13: Some quantifiable goals require model-based analysis performed outside the parametric modeler. Autodesk Ecotect© is used to determine average daylight values in lux for all three alternatives. Note that the ceiling is omitted for clarity ...... 91 Figure 3-14: Alternatives Analysis Model: a) users input impact scores and geometric parameter values for each analyzed alternative; b) the system generates a value score for each alternative. Alternative 1 emerges as the preferred one based on the goals identified in the RM and the goal importance determined by stakeholders 92 Figure 3-15: Jeddah mixed-use towers project in Saudi Arabia ...... 94 Figure 4-1: Tower 1 site configuration, initial tower/podium footprint, and the required 20m setback ...... 110 Figure 4-2: Components describing the high-level structure of the Tower 1 parametric CAD model...... 111 Figure 4-3: Tower 1 parametric model: a) the tower footprint (plan view) instantiated twice with input parameters controlling the tower rotation; b) tower envelope (perspective view) lofted from the three footprints; c) small section of the footprint with a column component and the driving parameters (perspective
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view); d) columns extruded along the footprint (perspective view); e) final model of a single floor used to create ~1,000 design options (perspective view)...... 112 Figure 4-4: The team investigated three structural alternatives using the same parametric CAD model. a) originally proposed solution with expensive double curvature; b) intermediate solution with single curvature but with architecturally unappealing columns; c) final solution with single curvature stepping columns, in which fins cover the step from top to bottom column. Column and fin sizes were varied to minimize the heat load. The glazing offset from exterior wall was adjusted to satisfy the gross area & efficiency constraints...... 113 Figure 4-5: Multiple Tower 1 alternative twist values were parametrically investigated during the design process. a) 60 degree twist; b) 90 degree twist; c) final design featuring a 90 degree twist and smaller glazing setback...... 114 Figure 4-6 (a – d): 7 alternatives from over 1,000 generated options. The lack of a formal methodology for defining and translating design requirements into parametric models led to the construction of six unique models to generate 20 alternatives...... 116 Figure 4-7: Test case “half teardrop” site and the “half teardrop” scenario tower footprint ...... 120 Figure 4-8: Requirements Model inputs. Project stakeholders: (a) determined 7 quantitative constraints and (b) 5 quantitative and qualitative goals; (c) each stakeholder indicated his/her preference for the 5 identified goals by distributing 100 percentage points. Note that this example is showing only the Design Architect preferences...... 121 Figure 4-9: Requirements Model outputs - the system generates the goal importance graph and normalized decision makers’ preferences to 100 points...... 122 Figure 4-10: “Half teardrop” Scenarios Model for constraint No. 1 – design architect decomposed the constraint into Action Items, Strategies, Parameters, and Parameter Constraints and determined how these relate to each other. Faded nodes indicate strategies considered, but not chosen, to be implemented in the parametric model...... 124
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Figure 4-11: SM output – Actions impact on requirements graph. ‘Control half teardrop configuration”, “ Control tower orientation and “Control unit width” emerged as Action Items that impacted most project requirements...... 125 Figure 4-12: Component-level PPM showing the parametric model structured into 18 components...... 127 Figure 4-13: a) Detail-level PPM for the “half teardrop” scenario showing the composition of the “Floor Plate” component; b) Test case tower floor plate preview...... 128 Figure 4-14: ISR analysis false color map (blue indicates less radiation, red – more); a) Alternative 1 – Worst (197,090 Wh/m2); b) Alternative 7 – Best (107,143 Wh/m2); c) Alternative 10 – surprising outcome (168,436 Wh/m2)...... 131 Figure 4-15: Key parameters affecting the amount of direct solar radiation accumulated by the tower’s exterior ...... 131 Figure 4-16: Daylight analysis – false color map (blue indicates less daylight, yellow – more); a) Alternative 1 – Worst (4,022,200 lux / floor); b) Alternative 8 – Best (2,205,150 lux / floor); Alternative 10 – surprising outcome (3,411,600 lux / floor) ...... 132 Figure 4-17: AAM “half teardrop” design scenario; a) impact scores for 10 design alternatives and 5 goals; b) system generated value scores – overall, Alternative 7 emerged as the most successful ...... 133 Figure 4-18: Alternative 1 of “Triangular” design scenario ...... 134 Figure 4-19: AAM “Triangular” design scenario; System generated value scores – overall, Alternative 8 emerged as most successful ...... 134 Figure 4-20: Scenarios Model for constraints No. 2 & 3 ...... 142 Figure 4-21: Diagrams illustrating how the balcony lengths were calculated balcony perimeter length on north side; b) balcony perimeter arc length on south sides 143 Figure 4-22: Scenarios Model for constraint No. 4 ...... 144 Figure 4-23: Scenarios Model for constraints No. 5, 6, 7 ...... 144 Figure 4-24: Scenarios Model for Goal No. 1 ...... 145 Figure 4-25: Scenario Model for Goals No. 2, 3 ...... 146
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Figure 4-26: Scenario Model for Goals No. 4, 5 ...... 147
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Chapter 1: Introduction
The motivations behind this Ph.D. research are the process problems I encountered while practicing conceptual design of buildings. As one of the pioneers of employing parametric Computer Aided Design (CAD) methods in the conceptual design of buildings, I discovered that current design process methods cannot be used to build effective parametric CAD models. To fully leverage the potential of parametric modeling in conceptual design of buildings, I developed a methodology called Design Scenarios (DS), which I present in this dissertation.
The dissertation is organized into four chapters and follows the three-journal paper format. The format has some redundancy because each paper stands on its own. Chapter 1 illustrates what motivated and how I organized and conducted my research and concludes with a summary of the research contribution. Chapter 2 is the first journal paper, in which I benchmark current non-parametric conceptual design processes. I propose an assessment method and metrics, which I retrospectively apply on five industry cases. I support the findings with a survey of industry leading Architecture Engineering design practices. Chapter 3 presents the second journal paper in which I introduce the Design Scenarios methodology and the ontology required to build multi-stakeholder driven parametric models and a software prototype to enable measuring the methodology’s impact. Chapter 4 presents the third journal paper. I first give an overview of two industry test cases to benchmark the application of parametric CAD with existing, non-parametric conceptual design process. I continue the paper with presenting the application of Design Scenarios methodology on one industry test and gauge the methodology’s impact by comparing four data sets: (1) Current non- parametric process; (2, 3) Current parametric process with traditional design methods; (4) Improved parametric process with DS methodology.
To structure my research I used the seven-step Center for Integrated Facility Engineering (CIFE) “horseshoe” method (Fischer, 2006) illustrated in Figure 1-1. The
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“horseshoe” is an iterative method, in which the process steps are constantly revisited to ensure correlation (shown with dotted arrows in Figure 1-1) and consistency.
Figure 1-1: CIFE “horseshoe” research method.
I next give an overview of the “horseshoe” process.
1 Observed problem Conceptual design is a complex process of building design spaces. Clevenger and Haymaker (2010) define a design space as consisting of four subspaces with elements and relationships among them (Figure 1-2).
Figure 1-2: Theory defines design space in terms of four subspaces with elements and relationships among them.
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The Objective Space includes the many stakeholders (e.g., developer, city planning department, end user) and designers (architect, structural engineer, mechanical engineer) engaged in a project. Stakeholders and designers determine project constraints and goals and assign preferences to goals. The Alternative Space consists of scenarios, alternatives, and options (Figure 1-3a). A scenario is a set of constraints restricting the design space (e.g., half teardrop shaped high-rise building).
a) b)
Figure 1-3: a) The Alternative Space consists of scenarios, alternatives, and options; b) example of impact of alternatives given the goals and constraints.
In the Impact Space designers determine the performance of alternatives given the goals and constraints. For example, if the goal is to maximize the number of apartments facing the sea in a building, the impact of the two alternatives (0 degree, and 90 degree rotation) in Figure 1-3b can be determined. The Value Space determines the alternative offering the best value for the objective space. Alternative 2 in this case offers the best value for the stated goal.
Research fields such as Design Theory (Krishnamurti, 2006) and Systems Engineering (Lamsweerde 2004, Colombo et. al. 2007) argue that good designs emerge from well- defined and comprehensive design spaces measured by space-specific elements (Figure 1-4). Chapter 3 specifies the elements and relationships necessary for the well- defined and comprehensive spaces. For example, the logic designers use to address the objective space is well-defined if it is clear and connected to elements in other spaces
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(e.g., goals) and is comprehensive if all stakeholders participated in developing this logic.
Figure 1-4: Design Theory and Systems Engineering say that design spaces need to be both well-defined and comprehensive.
How well does the industry build these design spaces today? In a typical one month long conceptual design process, with non-parametric methods design teams can generate on average only three alternatives (Gane and Haymaker, 2010). Parametric CAD, on the other hand, offers the ability to generate thousands of alternatives (Kolarevic, 2003). However, in addition to generating large quantities of parametric alternatives to improve the probability of identifying alternatives with higher value as motivated by Design Theory (Akin, 2001), it is important to make sure such alternatives are driven by objectives, and analyzed for multidisciplinary impacts.
Today there exists a gap between the world of design and computing, in which designers’ concepts need to be translated into computable models. Goals and constraints are neither clear nor defined by all stakeholders. CAD experts therefore struggle to build requirements-driven CAD models because they don’t have a well- defined and comprehensive set of input and output parameters and designer’ logic for addressing Objective Spaces. Furthermore, designers cannot use such models to select among alternatives with well-defined impact and value measures. I describe two industry test cases motivating this problem in Chapter 4.
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My intuition for addressing the industry problem is to develop and capture parametric scenarios in a relational network connecting objective, alternative, impact, and value spaces (Figure 1-4).
2 Research questions To address the industry problem, this dissertation answers three main research questions:
1. How well-defined and comprehensive are traditional conceptual design spaces? 2. How well-defined and comprehensive are existing parametric conceptual design spaces? 3. What is an ontology and method for designers and CAD experts to develop well-defined and comprehensive parametric alternative spaces that connect well-defined and comprehensive objective, impact, and value spaces? 4. What is the impact of the proposed method on the parametric conceptual design process?
3 Points of departure I started the development of the proposed ontology and method to construct well- defined and comprehensive alternatives spaces by first assessing the power of several relevant research fields (Figure 1-5). None of these fields enables constructing well- defined and comprehensive alternative spaces that connect the four spaces. For example, one of the well-known methods comprising the field of Systems Engineering is Quality Function Deployment (QFD), which enables developing well-defined and comprehensive objective, impact, and value spaces but not alternative spaces. With QFD designers cannot define scenarios or explicitly capture their logic used to address goals and constraints.
I developed the Design Scenarios methodology to fill this gap and enable connecting the objective and value spaces through the alternative space.
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Figure 1-5: Selection of theoretical points of departure used in this dissertation.
4 Research method / tasks Developing the methodology and the software prototype was a significant research task. DS methodology builds models for each of the four spaces (Figure 1-6). I divided the alternative space into two subspaces: (a) logic, where the designer’s logic is captured, and (b) geometry, where the CAD experts use designer’s logic to build requirements-driven parametric models.
To reflect the multi-stakeholder nature of the design process, each model contains various enablers, who are either humans or the computer. Enablers (e.g., Designer) generate elements (i.e., Constraint) with a set of methods (e.g., Create objective). The selection of methods and elements vary for each model with a total of 30 methods and 29 elements. Figure 1-6 does not include the methods, which are illustrated in Figures 1-7 – 1-11. I used observations of the concepts design teams use in industry and literature review to build DS elements. For example, MACDADI (Haymaker et. al.
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2006) and Requirements Engineering (Lamsweerde, 2000) provided the foundation for the elements in the objective, impact, and value spaces. On the other hand, I motivated the elements in the alternative space logic based on my knowledge and understanding of the concepts design teams currently use implicitly in the industry as well Points of Departure such as Requirements Engineering (e.g., First Order Logic).
Figure 1-6: Design Scenarios Methodology schema and connections among four spaces.
DS is an iterative process of building interconnected models for each of the five spaces. The enablers in the objective space are the project stakeholders and designers who determine goals and constraints and assign preferences to goals in a tabular model environment. Figure 1-7 illustrates the DS set of enablers, methods, and elements for building objective space models. In the software prototype the objective space is captured in the Requirements Model.
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Figure 1-7: Objective space enablers, methods, and elements.
The enablers in the alternative space logic are the project designers, who need to define how they intend to address objectives parametrically. In a prescriptive process modeling environment, designers create decision nodes that capture their logic for addressing the goals and constraints. Figure 1-8 illustrates the DS set of enablers, methods, and elements for building alternative space logic models. In the software prototype the alternative space logic is captured in the Scenario Model.
Figure 1-8: Alternative space logic enablers, methods, and elements.
The enablers in the alternative space geometry are the CAD experts. In a descriptive process modeling environment CAD experts connect well-defined and comprehensive
8 Chapter 1: Introduction Victor Gane designers’ logic represented by input and output parameters to computable parametric models. Figure 1-9 illustrates the DS set of enablers, methods, and elements for building the structure of CAD models. In the software prototype the alternative space geometry is captured in the Parametric Process Model, which CAD experts use to construct the CAD model and generate design alternatives.
Figure 1-9: Alternative space geometry enablers, methods, and elements.
The enablers in the impact space are the designers. In a tabular model environment designers record the impact scores for the generated alternative based on how each alternative meets objective targets. Figure 1-10 illustrates the DS set of enablers, methods, and elements for building impact space models. In the software prototype the impact space is captured in the Alternatives Analysis Model.
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Figure 1-10: Impact space enablers, methods, and elements.
The enabler in the value space is the computer, which builds a tabular model determining the overall value score for every generated alternative by summing the product of the cumulative importance of every goal with the impact score of that goal. Figure 1-11 illustrates the DS set of enablers, methods, and elements for building value space models. In the software prototype the value space is captured in the Alternatives Analysis Model.
Figure 1-11: Value space enablers, methods, and elements.
5 Research results / validation I used the Action Research (Hartmann et. al. 2008) and Case Study methods to benchmark how well-defined and comprehensive are the objective, alternative, impact, and value spaces for existing non-parametric, parametric, and DS-supported parametric conceptual design processes. I was the embedded researcher on eight industry test cases: (a) five projects benchmarking existing non-parametric design process (one shown in Figure 1-12a); (b) Infinity Tower in Dubai, UAE, and (c) Transbay Tower in San Fransicsco benchmarking existing parametric design process; (d) Jeddah Towers in Jeddah, Saudi Arabia benchmarking DS-supported design process. All test cases were Skidmore Owings and Merrill projects. On the Infinity and Transbay Tower test cases I had the roles of a designer and CAD expert and
10 Chapter 1: Introduction Victor Gane performed a retrospective analysis of the conceptual design process with no formal methods of employing parametric modeling. The Jeddah Towers was a prospective test case, in which I implemented Design Scenarios Methodology. I first interviewed all project stakeholders to determine their scenarios, goals, and constraints, as well as the designers’ logic to address requirements and the resulting input and output parameter set. As a CAD expert I used the identified parameters and well-defined logic to build parametric models for two scenarios, and generated requirements-driven design alternatives, for which I measured the performance and determined the total value.
a) b) c) d)
Figure 1-12: a) Tyrol Tower, Worgl, Austria; b) Infinity Tower, Dubai, UAE; c) Transbay Tower, San Francisco; d) Jeddah Towers, Jeddah, Saudi Arabia. Images courtesy of SOM.
The results of the validation provide evidence that the DS method provides CAD experts with well-defined logic and parameters for addressing project requirements and the process enables creating parametric alternatives with clear multi-objective values that potentially provide clients with better building designs.
6 Contributions to knowledge The two primary contributions to knowledge of this research are:
. Ontology to enable designers and CAD experts to develop well-defined and comprehensive alternative spaces. The ontology consists of elements (e.g., Action Item, Strategy, Parameter, Parameter Constraint, Logic Gate), which I
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developed by combining industry knowledge and theory. Figure 1-6 illustrates the ontology in which the novel elements are highlighted. . Design Scenarios Methodology (DS) to enable design teams connect through alternative spaces the project requirements from the objective space to well- defined and comprehensive values of alternatives determined in the value space. DS consists of model-specific enablers (e.g., CAD expert in the alternative space geometry), methods (e.g., draw geometry node), and elements (e.g., geometric primitive). Elements in the five DS models are interconnected (e.g., parameters in the alternative space logic and parameters in the alternative space geometry). Figure 1-6 illustrates the connections among the five models through the horizontal highlighted arrows.
7 Predicted practical impact This research determined that current conceptual design process is characterized by poorly defined, incomplete, and disconnected objective and value spaces. I anticipate DS methodology to enable design teams to:
. Develop comprehensive, multi-stakeholder project requirements; . Guide the designers’ alternative generation process and help CAD experts build better parametric models; . Provide clients with better performing building designs by clearly understanding the difference in the overall value of each generated design alternative; . Improve productivity of design teams and greatly reduce the number of negative iterations.
Figure 1-13 illustrates a selection of quotes from the industry leaders in support of the anticipated impact of DS methodology.
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Figure 1-13: Quotes collected from DS validation on industry projects.
8 Conclusion The validation of DS methodology has several limitations. I was the only researcher to implement the methodology and perform the measurements, which come from a single industry test case. Therefore, future work is to extend external validity by having design teams apply the DS methodology on more industry projects. DS also offers opportunities for automating the generation of the parametric CAD model from the Parametric Process Model, as well as integrate and automate the impact section of the Alternatives Analysis Model with multidisciplinary optimization methods.
Design Scenarios bridges two important worlds of design and computing. For example, researchers at the Design School at Stanford follow and develop more human centric design methods as opposed to researchers at the Center for Integrated Facility Engineering, who take a more mechanistic approach to design. I developed Design Scenarios method to enable designers to combine both approaches and integrate human thinking and creativity into a formal but flexible structure to support creative and systematic design exploration.
9 References Akın, Ö. (2001). “Variants of design cognition”, Design Knowing and Learning: Cognition in Design Education. Eastman, C., Newsletter, W., & McCracken, M., Eds., pp. 105–124. New York: Elsevier.
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Chachere, J., Haymaker J. (2011). “Framework for measuring rationale clarity of AEC design decisions.” Journal of Architectural Engineering, doi: 10.1061/ (ASCE) AE.1943-5568.0000036.
Clevenger, C., Haymaker, J. (2011). “Metrics to assess design guidance.” Design Studies, doi:10.1016/j.destud.2011.02.001.
Colombo, G., Mosca, A., Sartori, F. (2007). “Towards the design of intelligent CAD systems: An ontological approach”. Advanced Engineering Informatics 21, pp. 153–168.
Fischer, M. (2006). Formalizing Construction Knowledge for Concurrent Performance-Based Design. Intelligent Computing in Engineering and Architecture, 4200:186-205. Berlin / Heidelberg: Springer. http://dx.doi.org/10.1007/11888598_20 last accessed on May 12, 2010.
Gane, V., Haymaker, J. (2010). “Benchmarking current conceptual high-rise design processes”, ASCE Journal of Architectural Engineering. Vol. 16, No. 3. pp. 100-111.
Haymaker, J. and Chachere, J. (2006). “Coordinating Goals, Preferences, Options, and Analyses for the Stanford Living Laboratory Feasibility Study,” Intelligent Computing in Engineering and Architecture 13th EG-ICE Revised Selected Papers, Lecture Notes in Computer Science, Vol. 4200/2006, Ian Smith (ed.), Springer-Verlag, Berlin, Heidelberg, New York, pp. 320-327.
Kolarevic, B. (Ed), (2003). “Architecture in the Digital Age: Design and Manufacturing”. Taylor & Francis.
Krishnamurti, R., (2006), “Explicit design space?” Artificial Intelligence for Engineering Design, Analysis and Manufacturing. Vol. 20, pp. 95-103.
Lamsweerde A (2000). “Requirements engineering in the year 2000: A research perspective”. In: Proceedings of 22nd International Conference on Software Engineering, (ICSE’2000): Limerick, Ireland, Invited Paper, ACM Press, pp. 5–19.
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Lamsweerde A (2001). “Goal-Oriented Requirements Engineering: A Guided Tour”. Proceedings RE’01, 5th IEEE International Symposium on Requirements Engineering, Toronto, pp. 249-263.
Ross, D., Schoman, E. (1977). “Structured Analysis for Requirements Definition”. IEEE Transactions on Software Engineering, Vol. SE-3, No. 1, pp. 6-15.
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Chapter 2: Benchmarking current conceptual design processes
Victor Gane, John Haymaker
1 Abstract This paper presents an analysis of current conceptual design processes for high-rise buildings. We synthesize a method to document and measure these processes and use it to analyze data from several case studies and a survey of leading architectural and engineering design firms. We describe current high-rise conceptual design process in terms of the following: design team size, composition, and time investment; clarity of goal definition; number and range of design options generated; number and type of model-based analyses performed; and the criteria used for decision making. We identify several potential weaknesses in current design processes including lack of clarity in goal definition and a low quantity of generated and analyzed options. We argue that potentially higher performing designs are being left unconsidered, and discuss the potential reasons and costs.
2 Introduction: Need for Effective Conceptual High-Rise Design Processes The twentieth century experienced an unprecedented demographic shift. The world population more than doubled in the last 40 years. A 2004 United Nations report predicts that by 2050 the world population is expected to exceed 9 billion, and by 2010 the world’s urban population for the first time will surpass the rural one. Across the globe, low-density urban sprawl is the solution to population growth. There is mounting consensus among the scientific community that urban sprawl has significant negative social, economic and environmental implications (McElfish 2007, Kienast et.al. 2003). Four billion additional people will need housing and work places in just a few decades. An immediate way to address population growth is for cities to support high-density buildings, particularly high-rises. Over the past century, high-rises have successfully and increasingly responded to this need. We calculate that housing for 4
16 Chapter 2: Benchmarking current conceptual design processes Victor Gane billion people will require constructing close to 4 million 40-storey high rise buildings (each with 1,050 occupants in 350 units).
Yet most high-rises perform poorly in terms of lifecycle cost, environmental impact and social benefit. According to Yeang (1996), in a 50-year lifecycle of a high-rise, energy costs contribute 34% of the total cost. Close to 50% of energy use in high-rises comes from artificial illumination (Chartered Institution of Building Services Engineers 1997). Kaplan (2004) indicates that a typical high-rise building is made of poor quality materials and is aesthetically mundane. Successful high-rise designs need to use a minimum of nonrenewable energy, produce limited pollution, and minimize their carbon footprint, without diminishing the comfort, health, functional needs, and safety of the people who inhabit them.
To respond to these mounting environmental, economic, and social pressures, the Architecture, Engineering, and Construction (AEC) industry needs to revise traditional high rise design and analysis methods. Recent advances in computer-based methods promise vastly improved design processes, but current teams are ill equipped to take advantage of these new opportunities. To help them understand the reasons behind current inefficiencies and develop and implement more integrated design and analysis methods we must first document and measure existing conceptual high-rise design processes in terms of quantifiable metrics on which to base and compare the performance of prospective improvements.
Little research has been carried out in this area; the goal of this paper is to fill the gap. Through literature review and industry-based case studies, this paper develops a definition and relevant metrics describing conceptual high-rise design processes, and applies this definition and metrics to a set of contemporary case studies and survey data. We find that conceptual design teams generally operate with low project goal clarity, and generate very few formal design options and analyses that neglect environmental and life-cycle economic considerations. We conclude with a discussion about the potential causes and costs of today’s greatly underperforming high-rise design processes.
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2.1 Points of departure: What are current high-rise design processes and how do we document and measure them? In this section, we look to design theory for a theoretical definition of high-rise design processes; to process modeling for a method to describe and measure these processes; and to high rise-specific literature for classification and key design criteria.
2.1.1 Design theory
Akin (2001) formulates conceptual design as a five-step process: 1) identifying a set of requirements; 2) prioritizing among these requirements; 3) developing preliminary solutions; 4) evaluating solutions; and 5) establishing final design requirements, preferences and evaluation criteria. Haymaker and Chachere (2007) further formalize these distinctions in the MACDADI (Multi-Attribute Decision Assistance for Design Initiatives) framework, which includes: 1) organizations – a project’s stakeholders, designers, gatekeepers and decision makers; 2) goals – these organizations’ constraints, objectives, and preferences; 3) options – design options and methods to generate them; 4) analyses – the methods, timing, and types of analyses performed; and 5) decisions – rationale and process for making decisions. This paper is structured using these MACDADI distinctions.
Design is an unbounded process; there are infinite numbers of organizations, goals, options, analyses, and decisions that a team potentially can consider. Simon (1991) in his behavioral theory of bounded rationality describes people as partially rational when making decisions, due to computational limitations in gathering and processing information. Woodbury and Burrow (2006) also argue that designers typically consider a very small number of alternatives in their work as a result of cognitive limits. As a result, designers often make decisions without fully understanding their implications.
To develop solutions, designers first establish a design space. Krishnamurti (2006) defines a design space as the sum of the problem space, solution space, and design process. A problem space includes only the candidate solutions that satisfy the established design requirements. A solution space includes all candidate solutions for
18 Chapter 2: Benchmarking current conceptual design processes Victor Gane a given design problem. A design process consists of methods used to develop candidate solutions from requirements. The extent of the design space is highly dependent on the designer’s interpretation of the design problem, the choice of design criteria (project goals and constraints), and the employed design process. Two prevailing strategies emerge to describe the design process: breadth first, depth next or depth first, little breadth. The breadth first strategy entails generating multiple design options first, and then analyzing them to determine which ones meet the sought requirements. Depth first strategy entails generating a single option and analyzing it in depth.
Goldschmidt (2006) argues in favor of the depth versus breadth strategy, in which both known architects and novice students deliberately choose a limited design space to conduct their exploration. The goal is refining and enriching a “strong idea” supported by well-developed design rationale. In contrast, Akın (2001) argues that in solving problems expert designers prefer the breadth first, depth next strategy. As a result, multiple alternatives help reveal new directions for further exploration. Each strategy has significant implications in the way teams generate designs. Currently there is no consensus about which strategy performs best, although in light of rapidly evolving project teams, goals, options, and analyses, many researchers argue that the sheer quantity of options generated by a breadth first search enables designers to find more successful solutions in terms of multi-criteria and multidisciplinary performance (Sutton 2002).
Design theory helps us understand design processes, but it does not help us understand how to specifically represent and measure them. A widely accepted method for this kind of representation and analysis is process modeling.
2.1.2 Process modeling
There are three general applications for process models: a) descriptive: for describing what happens during a process; b) prescriptive: for describing a desired process; and c) explanatory: for describing the rationale of a process (Rolland et.al 1998). Froese (1996) presents a comprehensive overview of AEC-specific process models, including
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IRMA (Information Reference Model for AEC, BPM - Building Project Model), ICON (Information / Integration for Construction), and GRM (Generic Reference Model). Most of these are based on EXPRESS-G modeling language (International Alliance for Interoperability), which provides a foundation for graphically representing process models. Other significant process modeling languages relevant to this paper are: IDEF0 (Integrated Definition Models), used to model decisions, actions, and activities of an organization or system; Narratives (Haymaker et. al. 2004), which model information and the sources, nature, and status of the dependencies between information; and Value Stream Mapping (Tapping et. al. 2002), which describe the flow of actors, activities, task duration, and information that produce value in a given process.
With these process-modeling languages, we can describe design processes but not establish process measurement metrics. To address this problem various techniques have been proposed; for example, to evaluate BIM (Building Information Model) users practices and processes (McCuen et. al. 2007), to measure benefits from VDC (Virtual Design and Construction) use and factors that contribute to its successful implementation (Kunz et. al. 2007), or to simulate the impact of improvements to the engineering design process (Sosa et. al. 2002).
In spite of the wealth of existing process modeling languages, none can describe and measure design processes in terms of all the distinctions included in the MACDADI framework. This paper later synthesizes a process model and metrics to describe high- rise design team size and composition, the clarity of their goal definitions, the number of design options they generate and analyze, the prevailing objectives used in their decision making, the conceptual design duration, and the discipline-specific time invested.
2.1.3 High-rise classification and key design criteria
In the late 19th century a wave of innovations in the building industry led to the development of the first high-rises in Chicago and New York (Frampton 1992). The elevator, the steel frame, and later the curtain wall and HVAC, along with the demand
20 Chapter 2: Benchmarking current conceptual design processes Victor Gane for new office space on expensive and limited land, made the development of high- rises possible and necessary. Despite the success of high rises, the AEC industry lacks a consistent definition of the building type. The American Society of Heating, Refrigeration and Air-conditioning Engineers (ASHRAE 1989) defines high-rises as buildings in which the height is over three times the width, whereas structural engineers define high-rises as buildings influenced primarily by wind loads (Council on Tall Buildings and Urban Habitat 2000). High-rises can be categorized according to their function, structural system type, and environmental control strategies. From a functional standpoint, there are four types: residential, commercial, hospitality, and mixed-use. This section briefly describes what is currently known about the organizations, goals, options, and analyses of high-rise design processes.
Organizations
In high-rise design the developer is often the main decision maker. The developer outlines the architectural program and the budget constraints, and may specify a desired design language and construction start date. Future tenants are often involved at the conceptual design stage, and many cities require a design to be approved by neighbors. Gatekeepers such as city planning and building departments determine building height and construction limitations.
Design firms involved in the design process include architects, structural and mechanical engineers; later design phases include other consultants such as landscape, egress or LEED. The majority of design firms are single disciplinary, offering either architectural or engineering services, and are typically organized in a hierarchy. Figure 2-1 illustrates a representative design team hierarchy commonly found in high-rise design practice. A design director makes high-level design decisions that help determine the design space and therefore guide the design team, and represents the firm in client review meetings. Any decisions made at such meetings are then conveyed verbally or through sketches to the senior design and technical architects who oversee the design process. Most of the drawings and calculations are done by mid level and intern architects. The coordination between engineering and architectural drawings is generally done by the senior technical architect.
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Figure 2-1: Typical stakeholders and design team organizational hierarchy.
Goals (design criteria)
Several key design criteria must be considered when designing a high-rise. The Floor- Area-Ratio (FAR) is calculated by dividing the gross floor area allowed on a site by the net area of the site. FAR helps designers determine the maximum allowable building height. Building area efficiency is calculated as a subtraction of the building’s non-sealable area (core, circulation corridors, etc.) from its gross area. Generally, this number must be at least 75% and represents the net saleable or rentable area. The lease span, defined as the distance from the unit’s inner wall to the exterior glazing, varies according to the building function. For residential high-rises, a maximum lease span of 10m is recommended given the daylight factor considerations. Office buildings with an open plan allow for deeper spans of up to 14m or more when atriums are provided. In the case of modular office layout a building depth of over 13m is considered excessive (Eisele et. al 2002).
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The main criterion in choosing a load-bearing system is the lateral stiffness for resisting the wind and earthquake forces, which is governed by the total height (Schueller 1986). Additional criteria include building height-to-width aspect ratio, floor-to-floor height, interior layout, exterior wall, foundation systems, fire safety, construction methods, and budget constraints.
The criteria in choosing the foundation type are the gravity loads, quality of the site’s subsoil, water table level, and wind loads. Wind loads lead to significant vibration in the upper floors, which provides additional stresses on the bed soils (Ulitskii et. al. 2003). The foundation piles often determine the location of the structural grid and therefore may affect the overall building efficiency.
Fire safety is another important criterion. The core design needs to satisfy the number and size of escape stairwells by determining the building occupancy, as well as address the smoke extraction. Budget constraints often influence the structural material choice.
Multiple design criteria exist to help improve environmental control strategies of high- rises. High level concepts include maximizing reliance on natural ventilation and daylight illumination, while -- depending upon the season and geographical location -- minimizing or maximizing the heat gain from direct sunlight along the building’s perimeter areas.
Options
Facades have a significant influence on the aesthetics and symbolism of high-rises (Council on Tall Buildings and Urban Habitat 2000). Two main types can be distinguished: curtain wall (butt glazed, conventional mullion system, composite - glass and cladding), and facades as expression of structure (reinforced concrete, prefabricated panels, exoskeleton, etc.).
Circulation patterns influence the building’s efficiency. They are determined by the configuration, number, and positioning of the service core(s) (i.e., single or multiple internal or external). A typical core includes elevators (i.e., passenger, service, dedicated to specific functions), fire-protected stairs, electrical / cable closet, riser
23 Chapter 2: Benchmarking current conceptual design processes Victor Gane ducts, and sometimes washrooms. The core positioning will determine the floor plan configuration, given the maximum allowable distance from the outermost point of the corridor to the escape stair(s). When designing cores an important consideration is the elevator cab aspect ratio. The preferred range is between 1:2 and 1:3 for maximizing loading and unloading efficiency (Yeang 1996).
The Council on Tall Buildings and Urban Habitat (CTBUH) defines three major structural systems: steel, reinforced concrete, and composite. The choice of structural system determines the building aspect ratio limitations. Until recently, a 6:1 aspect ratio was a constraint (Khan 2004). Currently, there are precedents for 10:1 or higher (Pelli 1991). Floor heights in residential high-rises are generally smaller than in commercial, and range between 2.5 - 3.5m. In commercial high-rises, floor heights range between 3.3 - 4.5m. To maximize efficiency, interior layouts may often seek a minimal presence of structural elements, which help choose alternative strategies (exoskeleton, concrete tube, etc.) Generally, the architect’s choice of exterior wall system will impact the structural solution. Until recently, the rule of thumb in curtain wall-based high-rises was to vertically divide the façade into 1.5m increments due to ease of assembly, cost savings, and flexible planning. Today, geometrically complex high-rise designs demand variability in the exterior wall panel sizes and novel structural solutions (Morphosis 2006).
Engineers can choose among multiple foundation types depending on the building design and soil properties. Examples include cast-in-place telescoping piles, caissons, slab-pile, piled-raft, mat foundation, etc.
Cost often can determine the design’s final choice of material. For example, reinforced concrete is preferred in the developing world, given its cheaper cost and lower construction skill requirements than for steel structures.
Two types of high-rises can be distinguished according to employed environmental control strategies. First relies entirely on mechanical systems and is a net energy consumer. The second type responds to the climate and site context.
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The Chartered Institution of Building Services Engineers (CIBSE 1997) distinguishes four types of natural ventilation systems: a) cross ventilation, with windows on both sides; b) single-sided ventilation, with all the windows on one side; c) stack ventilation, in which fresh air is drawn through windows and hot air is exhausted through the roof; d) mechanically assisted, to increase the airflow in any of the first three systems. In high-rises stack ventilation is the preferred strategy given that the building’s height helps create a chimney effect. However, a good understanding of the environmental conditions is important, as, for example, in hot and dry climates the stack effect may not function during the day.
Light shelves or reflectors can be used to diminish the energy use in high-rises. Their performance is subject to optimal orientation, determined by the building orientation and geometry. Joachim (2006) presents an extensive study of how daylight is affected by the high-rise geometry and the floor plan proportions as related to the core design. He concludes that triangular footprints perform best by having the least amount of dark spaces within a 10m span, followed by the square and circular configurations. This study, however, is limited to centrally located cores. Yeang (1996) stresses the importance of peripheral core location on east and west facades used as thermal “buffer-zones,” leading to important reductions in air-conditioning loads and operation costs. Additional benefits include access to daylight and natural ventilation into the core area, making the building safer in case of power failure, and eliminating the requirement for mechanical fire-fighting pressurization ducts.
Considering their height and external surface area, high-rises are well suited to take advantage of emerging energy generation technologies. Integrating photovoltaic cells (PVs) into the building’s exterior wall or louver system, for example, may become feasible after understanding the local climate and context. This knowledge helps choose optimal building orientation and location of PVs (i.e., non-shaded sections of the building). Similarly, given substantial wind velocities at high altitudes, high-rises can be volumetrically shaped to maximize the performance of integrated wind turbines (i.e., Zero Energy Tower in Guangdong, China by SOM). Other technologies include
25 Chapter 2: Benchmarking current conceptual design processes Victor Gane geothermal energy and thermal storage, evaporative cooling systems for arid climates, etc.
Analyses and Decisions
Designing high-rise buildings is a complex process as illustrated by the criteria discussed above. As a result, many prototypes have been developed in academia and practice to address aspects of the design processes as heuristic rules that automate some of these processes and help designers make decisions more efficiently. Danaher (2000) argues that by not being well defined the conceptual design is reserved only to senior, experienced designers. He proposes the use of knowledge-based expert systems in facilitating the access of junior designers to expert knowledge, in which the system guides them towards good solutions. Several such systems surveyed by Danaher are Hi-Rise (Maher et. al. 1985), Tallex (Sabouni et. al. 1997), Conceptual (Haber et. al. 1990), Predes (Fleming et. al. 1990), and Archie-II (Domeshek et. al. 1992).
Ongoing research efforts address various aspects of conceptual high-rise design in practice. Baker (2008), for example, explores the use of novel, proprietary computational tools based on evolutionary structural optimization, genetic algorithms, etc. in generating topological structural studies of high-rises. Whitehead (2003) develops custom parametric tools to facilitate the design space exploration. The use of such tools has led to new architectural expressions. In addition, such challenging environmental performance criteria as energy, daylight, or natural ventilation can now be understood through the use of discipline specific model-based analysis tools (i.e. EnergyPlus, Radiance, Fluent).
Despite these promising developments, the AEC industry currently lacks case studies describing high-rise design processes and how well these processes perform. This lack of data makes it hard to understand the impact of the above-mentioned or future solutions on the overall conceptual design process.
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3 Methods: Documenting and Analyzing Current Practice This section explains our methods for describing and measuring the conceptual high- rise design process. We develop and adapt a process modeling language to record and communicate the design process, and collect metrics describing the process performance.
3.1 Documenting Current Conceptual Design Process Our research method involved using an embedded researcher (Hartmann et. al. 2008) on several high-rise design teams to observe and document practice. We developed our observations over five cases. Figure 2-2 describes the observed process used in one of the cases. To document our observations, we synthesized a process-modeling notation from IDEF0 and Narratives (Haymaker et. al. 2004). A typical process node shown in the legend in Figure 2-2 captures the actor(s) that performs the action (project manager, architect, structural engineer, etc.), the tool or method used to generate information or make a decision (CAD, team meeting, etc.), the abbreviated description of the performed action, the time range to perform the action, and finally the input information needed to perform the action as well as the resulting output information. If several actors are involved in implementing parts of the same process, the time tab indicates a cumulative value. The arrows to the input or from the output nodes indicate observed information dependencies.
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Figure 2-2: Process model describing the case study conceptual design. Nodes A & B describe the early stages, in which decisions about team size, composition, duration, and deliverables were made by the design firm owner, project manager, and senior designer. Nodes C, D, & E describe the process by which the design team evaluated the project context and requirements and proposed the first design concepts in a design charrette. Nodes F, G, H, & I describe the process by which the design team developed 2D working drawings to calculate whether area and building efficiency requirements were met and a 3D model for visual evaluation of the design. Designers repeated the process several times before these requirements were met. Nodes J, K, L, M & N show that only after the senior designer accepted a design option for final development were the mechanical and structural engineers involved, the geometry for physical model prototyping prepared, and the conceptual design package assembled. Mechanical and structural engineers rationalized the design rather than participated from the beginning in decision making. Adapted from SOM case study project.
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We use this notation to describe the conceptual design of the Tyrol Tower case study in terms of a process model shown in Figure 2-2 and analyzed in the following subchapter. In the case of Tyrol Tower the client commissioned a feasibility study for a multi-tenant hotel and office tower on a complex site in the Tyrolean village of Worgl, Austria. The tower site was contained by a roundabout 150m in diameter intended to serve as an exit hub from the Munich – Verona freeway (Figure 2-3).
Figure 2-3: Tyrol Tower site, Worgl, Austria. With permission from SOM.
The project duration was set to two weeks. The decision about the team size and project duration was determined by the project manager in conjunction with the firm’s managing partner based on the contract amount, profit target, available personnel, and the client-agreed requirements and schedule. This information was communicated both verbally and by email to the senior designer, who decided in advance on the team composition made of a senior architect, two mid level architects and a project manager. The design process started with a set of loosely defined constraints, such as the architectural program components (2 and 4 star hotel, offices), area requirements, and height limitation. During the kickoff meeting the senior designer assigned appropriate roles / tasks, established the required deliverables and milestones, and set the first design charrette date.
In between the kickoff meeting and the charrette, the designers researched the historical and cultural background of the Tyrolean region and studied the site context and the project brief. This allowed each individual team member to develop a design strategy before reconvening at the charrette, where after a review of the researched
29 Chapter 2: Benchmarking current conceptual design processes Victor Gane materials (images, maps, facts, etc.), the senior designer sketched two concepts (Figure 2-4a).
a) b)
Figure 2-4: a) Tyrol Tower concept sketches – skiing was the prevailing design theme; the first concept was influenced by the shape of the slalom ski poll, the second abstracted a skier in motion with a 2.5 degree inclination; b) massing, orientation, and tower positioning strategy were influenced by the site geometry, prevailing wind direction, and the need to link the tower to the adjacent site. With permission from SOM.
Both concepts reflected the region’s strong skiing tradition. The first was inspired by the shape of a slalom ski pole; the curve along the building height supported the architectural transition from the area between the 4- and the 2-star hotel (the latter required a smaller lease span). The second concept drew from the idea of a skier in motion, and called for a 2.5-degree forward tilt to convey the notion of speed and movement. Having agreed on the two design themes, the team proceeded to developing strategies for the building’s massing and orientation / position within the site (Figure 2-4b). The egg-shaped footprint was preferred over round, square, and elliptical configurations, because of the site geometry and the prevailing wind direction. The tower placement on the site was determined by the need to link it to an adjacent site with a bridge.
At the end of the charrette, the team agreed to pursue the second concept for further development. Such architectural considerations as aesthetics and context suitability
30 Chapter 2: Benchmarking current conceptual design processes Victor Gane were the dominant factors. Most of the work at this point was handled by the mid- level architects. Two parallel tasks generally emerged at this stage. First was the development of a set of 2D documents to help illustrate the design. These included preliminary plans of typical and unique floors (4 and 2 star hotel, amenities, mechanical, etc.) as well as the building core, and an axial section for communicating major design features, such as the atrium and the building tilt (Figure 2-5a, b). These floor plans made calculating the building area and efficiency possible.
a) b)
Figure 2-5: a) Tyrol Tower typical and unique floor plans; b) building section. With permission from SOM.
The second effort was to develop a 3D model that was graphically rendered and used to evaluate the design visually in its context (
Figure 2-6a). Renderings initially helped the design team to make design decisions and later to communicate the concept to the client.
a) b)
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Figure 2-6: a) Tyrol Tower conceptual renderings; b) physical model. With permission from SOM.
The two tasks were closely related, considering that both designers needed to constantly coordinate the generated information. Satisfying the area and aesthetic goals required three consecutive geometry adjustments (see Figure 2-2, f-i). The 2D drawings required partial reworking as opposed to the 3D model that had to be completely rebuilt.
Once the two architects, who developed the technical and visual materials, accepted the design candidate, the senior designer formally evaluated and approved the concept for final development. This process included reviewing color prints of the building renderings, a set of printed 2D floor layouts, a section, and a spreadsheet with area and efficiency calculations. The outcome was a set of minor hand sketched corrections and written recommendations.
However, the senior designer became interested in exploring a new idea, in response to a previously unarticulated goal of supporting natural ventilation. He suggested drastic changes to the geometry to include two scallops on the wide side of the building (Figure 2-7). Facing the prevailing winds, these were intended to act as air catchers. Such revision would have invalidated most of the completed work considering the static nature of the generated design information (i.e., 2D and 3D drawings / models) and the subsequent need to regenerate it. Furthermore, validating the newly proposed concept would have required mechanical engineers to run a formal CFD (Computational Fluid Dynamics) simulation. Given the time and effort required to perform such analysis, this task was perceived as unrealistic by the design team. With less than a week left before the submission deadline, the team jointly agreed not to pursue this option, even if it could have led to a more environmentally sound design.
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Figure 2-7: A late concept sketch proposed by the senior designer in response to the previously unarticulated goal of providing the building with natural ventilation. Implementing the proposed side scallops would have invalidated most of the completed work, and because the time constraints the idea was abandoned. With permission from SOM.
With natural ventilation becoming an important goal this late in the design process, mechanical engineers were instead asked to recommend a solution strategy for the originally developed option. The existing multidisciplinary collaborative model generally does not support rapid model reuse. In other words, the architect-generated geometry cannot be used by mechanical or structural engineers in their analyses. Consequently, no model-based analysis was performed by mechanical engineers because of the established process and the time constraint. Instead, they reused the architectural section drawing to graphically illustrate the concept of air circulation based on the stack effect (Figure 2-8 a, b). Similarly, structural engineers did not generate model-based analyses and were asked to verbally validate the architect- suggested structural concept of a centrally located reinforced concrete core and perimeter column system.
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a) b)
Figure 2-8: Information produced by mechanical engineers did not include any model-based analyses but rather a set of conceptual, untested recommendations, such as: a) stack effect diagram based on architect’s section; b) standardized graphs of annual wet and dry bulb temperatures, wind rose, and annual direct solar radiation to inform which months of the year were most suitable for natural ventilation. With permission from SOM.
After the design director approved the concept design, the 3D geometry was meshed and forwarded to a consultant in preparation for building the physical model. In this case, the material choice for the tower was aluminum and the process of milling the model was outsourced (Figure 2-6b). The site plan, however, was laser cut in house using the earlier produced 2D drawings. In parallel, the team started preparing the final presentation materials, which included coloring the earlier generated plans and section, photographing the physical model, assembling a board with the project metrics, and printing full-scale sheets for foam board mounting.
In summary, as shown in Figure 2-9, a team of four architects and three engineers were able to generate three design options in two weeks. The alternatives were variations of one design theme chosen for further development. The design process was primarily based on architectural constraints. No clear goals were established until close to the project submission deadline, making these impossible to implement. The performed analyses addressed only architectural concerns. Architects used metaphoric references on which to base their aesthetic analysis. They motivated the footprint shape and the orientation of the building volume based on knowledge of prevailing
34 Chapter 2: Benchmarking current conceptual design processes Victor Gane wind direction supplied by the mechanical engineers; however, no model-based analysis was performed to understand the airflow and pressure on the chosen design. Structural engineers were only verbally consulted.
Figure 2-9: Process performance metrics adapted from the SOM case study project.
The embedded observations enabled a detailed understanding of how current high-rise design processes are performed and managed. However, while our other case studies provided similar results, our sample size was small and limited to one firm on a handful of projects. The next section describes a survey we conducted to provide some evidence for the generality of our field observations.
3.2 A survey of industry professionals on current conceptual design process We conducted an online survey of 20 senior architects and structural and mechanical engineers from several leading AEC practices (Survey conducted between January- May 2008). The survey contained 20 questions about their project’s team size and composition, goal clarity, number and type of options generated and analyzed, and prevailing goals in decision making. Respondents were asked to specify the high-rise function. All four functional types were part of the survey, but almost 60% of answers were mixed-use type. Fifty six percent rated the projects as moderately complex, and 38% as highly complex. We described a moderately complex designs were defined as single curvature and including new building systems such as integrated wind generators. A design of high complexity was defined as having double curved geometry and using new materials and building systems.
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3.2.1 Organizations
On average, we found design team size is 12 professionals with 65% being architects (including project managers). Figure 2-10 shows on average over 70% of respondents indicated one professional in each of the positions described in Figure 2-1, with the exception of mid level and intern architects.
Figure 2-10: Average reported survey results of team size and composition. A typical design team includes 12 professionals, 65% of whom are architects. Most design and engineering positions were generally reported to be filled by a single professional with the exception of intern (3) and mid-level architects (2).
We next asked whether goals were defined and, if so, what were the means to communicate them (Figure 2-11a). Twelve percent of respondents’ answers indicated a lack of any goal definition, and 82% pointed to goals being defined and communicated only verbally.
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a) b)
Figure 2-11: Average reported survey results (% respondents) showing: a) the percentage of projects that employed each method of goals communication– goals on most projects were communicated verbally; b) the clarity of the identified goals – most had low to low / medium clarity (Note: respondents were allowed to choose multiple answers).
Next, respondents rated the clarity of the identified goals (Figure 2-11b). We differentiated among four levels of clarity: Low with a partial list without metrics defined; Low / Medium with a partial list with partial metrics defined; Medium with a complete list without metrics defined; and Comprehensive with a complete list with metrics defined. About 70% of answers indicated low to low / medium goal clarity. As one of the survey respondents pointed out, “Internal goals tend to be informal and more conceptual than specific.” The results confirm our observations from the case studies and help identify an important conclusion: even when goals are defined, they often lack clarity of target metrics.
Figure 2-12: Average reported survey results (% respondents) showing: a) who established project goals – mostly clients and architects with little or no participation from engineers. b) major project constraints – most were determined by the developer emphasizing commercial efficiency as opposed to lifecycle efficiencya confirms our field observations concerning those who participate in goal definition. Over 80% of respondents indicated the clients and close to 60% the architects, while structural and mechanical engineers have a low participation in defining goals. This may lead to important design criteria being omitted in early design decision making process and require substantial design adjustments in later design phases. A respondent indicated that “typically, the architect and the client discuss and agree on project goals and
37 Chapter 2: Benchmarking current conceptual design processes Victor Gane parameters. The contractor and engineering disciplines often contribute to the definition of these goals and refine and assess the metrics.”
a) b)
Figure 2-12: Average reported survey results (% respondents) showing: a) who established project goals – mostly clients and architects with little or no participation from engineers. b) major project constraints – most were determined by the developer emphasizing commercial efficiency as opposed to lifecycle efficiency. (Note: respondents were allowed to choose multiple answers).
Figure 2-12b illustrates which goals are generally addressed during conceptual design of high-rises. Most responses indicated a prevalence of traditional, architect-driven goals, such as creating a unique and iconic design, and for developer-driven goals, such as building efficiency, area requirements, and construction budget. Five percent of respondents indicated sustainable construction principles and energy conservation as additional goals.
3.2.2 Options
Figure 2-13a and b show that close to 60% of respondents indicated that only two-to- three options are generally produced, confirming our earlier case study findings. We defined a new design option as any change that requires generating new or updated architectural floor plans, elevations, sections, 3d model for generating simple renderings, conceptual physical model, and a range of model-based analyses discussed in the next subchapter. Over 90% of respondents indicated using such traditional tools as AutoCAD, 3D Studio, and Photoshop for option generation. Fifteen percent indicated the use of FormZ, Google SketchUp, and MS Access, among other tools.
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Fifty six percent of respondents did not experience significant differences between the time it took to generate each design option.
a) b)
Figure 2-13: Average reported survey results (% of respondents) showing: a) the number of design options generated during conceptual design – majority indicated three; b) tools used are traditional CAD or graphics software that support generating single, static solutions. Very few respondents used emerging technologies, such as parametric modeling or energy analysis tools. (Note: For question (b) the respondents were allowed to choose multiple answers).
3.2.3 Analyses
Figure 2-14 illustrates that when asked which specific analyses are currently performed at the conceptual stage of a high-rise one quarter of respondents indicated performing Computation Fluid Dynamics (CFD), Energy or Daylight analyses. However, our case study findings were confirmed by close to 78% of respondents, who chose architectural requirements as the leading analyses criteria. Over 6% indicated no analyses performed. Problems with interoperability of design and analysis tools and schedule constraints make the incorporation of engineering performance concerns difficult in conceptual design.
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Figure 2-14: Average reported survey results (% respondents) showing the model- based analyses performed during conceptual design. The performed analyses address predominantly architectural concerns (i.e., budget constraints, program requirements). (Note: respondents were allowed to choose multiple answers).
Our survey and case study findings both illustrate that the current conceptual design process is dominated by architectural concerns analyzed both in terms of quantifiable (i.e., cost, area, efficiency) and non-quantifiable metrics (i.e., aesthetics, context suitability).
3.2.4 Decisions
Now that we have discussed the number and clarity of project goals and how design options are generated and analyzed, we need to determine the criteria used in actually making concept design decisions (Figure 2-15). The survey results support our earlier findings. Such architectural criteria as aesthetics (100% of respondents), area efficiency (63%), and site views (56%) are predominantly used in current design decision-making process. A further 13% of respondents indicated it is harder to quantify architectural criteria, such as identity, character, and human values. Less than half of respondents indicated using structural or mechanical engineering performance criteria.
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Figure 2-15: Average reported survey results showing which objectives designers consider when making design decisions. Architectural criteria (i.e., aesthetics, area efficiency, site views) prevail over engineering performance criteria (i.e., energy efficiency, natural ventilation, structural performance).
3.2.5 Time investment
Figure 2-16 summarizes the overall concept design duration in weeks. While no dominant answer emerged, we conclude that in most conceptual design processes take between 4 and 6 weeks (supported by three quarters of respondents).
Figure 2-16: Average reported survey results (% respondents) showing conceptual design duration, which predominantly fluctuates between 4 - 6 weeks.
Figure 2-17a illustrates the time investment per design team member, while Figure 2-17b per discipline. Respondents were also instructed to include extra time invested beyond the 8-hour workday. A disproportionately large amount of the total design time is spent by architects (with an average of 1,850 hours) as opposed to by engineers. The average mechanical engineer contributes 30 hrs, the average structural engineer contributes 50 hrs, the average intern architect contributes 350 hrs, the
41 Chapter 2: Benchmarking current conceptual design processes Victor Gane average mid-level architect contributes 250 hrs, the average senior technical architect contributes 50 hrs, and the average senior design architect contributes 250 hrs.
a) b)
Figure 2-17: Average reported survey results showing: a) total man hours invested by each team member type during concept design phase; b) total number of hours by discipline.
Finally, we asked respondents to distinguish between percentages of the total time invested in each step of the analysis framework used in this paper. The results are shown in Figure 2-18. Fifty four percent indicated an average 10% of the total time is used on establishing project goals; 40% indicated that 50% of the total time is used on generating design options; 67% specified an average of 10% of the total time is dedicated to analysis of design options; and 74% of respondents specified 30% of the total time is used on final design decisions and preparation of concept design presentation materials. In summary, most of the time is spent on generating design options and preparing presentation materials. The least time is spent on goal definition and analyses.
Figure 2-18: Average reported survey results showing percentages of the total time spent on goal definition, option generation, analysis, and preparation of presentation materials.
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A summary of averaged survey results is shown in Figure 2-19. These triangulating survey results closely corroborate our case study observations presented in Figure 2-9.
Figure 2-19: Averaged survey concept design process performance metrics.
4 Conclusion: Potential cost of underperforming conceptual design processes The contributions of this paper are the new hybrid process models and quantified measurements describing current conceptual design processes. While these findings are limited to high-rise building type and for a limited number of case study and survey participants, which makes it difficult to claim generality, the presented models and metrics can help researchers and decision makers understand how complex architecture-engineering design processes perform, can guide research and development efforts to improve these measurements, and can serve as a benchmark for comparing new design methods, tools and processes.
The market economy requires us to design quickly and cheaply; however, researchers such as Sutton (2002) have shown that we can’t tell which new ideas will succeed and which will fail at the outset, and that successful design is largely a function of sheer quantity. Current design methods manage only a few potential designs without a deep understanding of their multi-attribute performance. When coupled with increasing complexity in the design requirements and available building technologies, design teams are doomed to produce underperforming buildings.
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We conclude this paper with a brief discussion of our intuitions about the causes of these underperforming processes, and discuss potential ways to improve these processes and the potential costs and benefits of doing this.
4.1 Organizations While we expect no significant changes in the composition of high-rise design teams and stakeholders, a potential area of improvement is the hierarchical organization of design teams, which determine who makes decisions as well as how and when. The AEC industry would benefit from having engineers play a more significant role in early interactions with stakeholders when crucial decisions about a future design are made. Scrum Agile software development methods (Schwaber et. al. 2001) are an example of more “democratic” and empowered cross-disciplinary iterative design, in which both requirements and solutions are collaboratively developed by inter- disciplinary teams. Another example is Concurrent Engineering design management system (Prasad 1995, Ma et. al. 2008), which is primarily used in aerospace industry and supporting parallelization of multidisciplinary tasks. The AEC industry is beginning to adopt new organizational and contractual structures (i.e., AIA Integrated Project Delivery) to encourage more integrated design processes, although these have not had a large impact in high-rise design practice.
4.2 Goals As illustrated, goals are often ambiguous and defined without the participation of all AEC disciplines. We believe the current goal definition model leads to significant inefficiencies in the overall conceptual design process. Both the client and architects may lack specialized knowledge to allow them to establish a comprehensive set of project goals. Furthermore, architects often clarify project goals during the design process. This may lead to unsystematic shifts within the design space due to important guidelines being omitted early in the design process. While some goal ambiguity may aid in creative design, a lack of initial goal clarity leads to starting the design process with broad design spaces that are hard to efficiently explore. As a result, few options are generated and analyzed in depth.
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In addition, verbal communication of established goals may lead to further omissions and misinterpretations, especially among the junior design team members. Understanding and managing these requirements early in the design process is a major challenge today. Consequently, the AEC industry would benefit from a formal methodology used to determine explicit design requirements to help guide the generation of design options.
4.3 Options Translating such requirements into a wide range of design options that designers can quickly analyze and choose from is essential. With current methods, a multidisciplinary team averaging 12 people can normally produce only three design options in 5 weeks - a poor result that we have discovered in other segments of the AEC industry as well (Flager et. al. 2007). Among possible causes are the unclear goal definitions and the prevalently used design tools that support developing only single, static solutions. The relationships among design information are difficult to establish, manage, and resolve with these tools, making design modifications hard to quickly coordinate. This explains why there is no significant difference in the time needed to develop new design options, once an initial design has been proposed. The AEC industry would benefit from a formal methodology to translate requirements efficiently into multiple design options.
4.4 Analyses The current inability to conduct multidisciplinary model-based analysis efficiently is in part caused by the nature of AEC tools. The design and analysis tools are not well integrated and require substantial time investment in structuring the information for discipline-specific needs. For example, the architect-generated geometry is generally unusable by structural engineers, who need to reconstruct it in a suitable format (i.e., wireframe with attributes describing material properties and load conditions). Current design approaches do not support efficiently calculating even rudimentary model- based analyses, such as cost or area, which in turn discourage designers from exploring a larger segment of the design space. Finally, engineers are normally
45 Chapter 2: Benchmarking current conceptual design processes Victor Gane engaged after architects have already chosen a preferred design option, which leads to inconsistencies in the types of analyses performed on each generated option. Consequently, the AEC industry would benefit from a design model that includes engineers much earlier in the conceptual design process to help develop robust design and analysis strategies, starting early in conceptual design.
4.5 Decisions Designers tend to use only a limited selection of high-rise design criteria when making early design decisions. Bounded rationality theory (Simon 1991) provides an explanation. Concurrent consideration of multiple criteria with today’s design methods overwhelms designers, who instead break down the problem into sub problems leading, according to Goldschmidt (2006), to partial interconnected solutions. These solutions are synthesized into adequate designs through multiple consecutive manual corrections as illustrated in the case study. The notion of adequacy, however, is compromised when the criteria used in making these decisions are architecturally biased.
This paper observed, through case studies and a survey, that design organizations during the conceptual design of high-rises treat goals informally and search through a relatively narrow part of the design space. Design theory and our own experience suggest that significantly better performing buildings are remaining undiscovered. Deficiencies in current conceptual design process lead to solutions with mediocre daylighting, and excessive thermal loads and energy demands, thus making the cost of operating or retrofitting traditional high-rises prohibitive. The lack of a comprehensive and systematic method of defining multi-stakeholder and multidisciplinary goals, managing their evolution, and generating and choosing among design options that respond to identified goals is a major impediment to more successful design.
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5 Bibliography Akın, Ö. (2001). “Variants of design cognition”. Design Knowing and Learning: Cognition in Design Education. Eastman, C., Newstetter, W., & McCracken, M., Eds., pp. 105–124. New York: Elsevier.
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Eisele, J., Kloft, E., (2002). “High-rise Manual - Typology and Design, Construction and Technology”. Birkhauser - Publishers for Architecture
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Fleming, J., Elghadamsi, E., Tanik, M. (1990). “A knowledge-based approach to preliminary design of structures”. Journal of Energy Resources Technology, ASME, Vol 1 12, 4, pp. 213 - 219.
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Haber, D., Karshenas, S. (1990). “Conceptual; An expert system for conceptual structural design”. Microcomputers in Civil Engineering. Vol. 5, 2, pp 119- 125.
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Haymaker, J., Chachere, J., (2007). “Coordinating goals, preferences, options, and analyses for the Stanford Living Laboratory feasibility study”. Intelligent Computing in Engineering and Architecture 13th EG-ICE Revised Selected Papers. Lecture Notes in Computer Science, Vol. 4200/2006. Springer-Verlag, Berlin, Heidelberg, New York, pp. 320-327.
Haymaker, J., Fischer, M., Kunz, J., Suter, B. (2004). “Engineering test cases to motivate the formalization of an AEC project model as a directed acyclic graph of views and dependencies”. Itcon, Vol. 9. Pp. 419-441.
Integrated Definition Methods. Accessed at http://www.idef.com/idef0.html
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Joachim, M. (2006). “Ecotransology - Integrated Design for Urban Mobility”. PhD Thesis, MIT.
Kaplan, D. (2004). “High Performance High-Rise Residential Buildings”. AIA Center for Building Performance. Symposium on Building Performance.
Khan, Y. (2004). “Engineering Architecture: The Vision of Fazlur R. Khan”. W. W. Norton & Company. pp. 260.
Kienast, F. Jager, J. (2006). “Degree of urban sprawl in Switzerland: Quantitative analysis 1940–2002 and implications for regional planning”. Swiss Federal Institute of Forest, Snow and Landscape Research Report; ETH.
Krishnamurti, R. (2006). “Explicit design space?” Artificial Intelligence for Engineering Design, Analysis and Manufacturing. Vol. 20, pp. 95-103.
Kunz, J., Gilligan, B. (2007). “Value from VDC / BIM Use”. Accessed at http://cife.stanford.edu/VDCSurvey.pdf
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Maher, M.L., Fenves, S.J. (1985). “Hi-Rise: A Knowledge-Based Expert System for the Preliminary Structural Design of High Rise Buildings”. Ph.D. Dissertation, Department of Civil Engineering, Carnegie-Mellon University, Pittsburgh, USA.
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Skidmore, Owings, & Merrill, (2007). “Transbay Tower”. San Francisco, CA. Competition entry.
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Rolland, C.; Pernici, C. (1998). “A Comprehensive View of Process Engineering”. Proceedings of the 10th International Conference CAiSE'98, B. Lecture Notes in Computer Science 1413. Pisa, Italy: Springer.
Sabouni, A.R., AI-Mourad, O.M. (1997). “Quantitative knowledge based approach for preliminary design of tall buildings”. Artificial Intelligence in Engineering, Vol. 11, pp. 143-154.
Schueller, W. (1986). “High-rise buildings structures”. 2nd edition, Robert E. Krieger Publication Company.
Schwaber, K., Beedle, M. Agile (2001). “Software Development with Scrum”. Prentice Hall.
Simon, H. (1991). “Bounded Rationality and Organizational Learning”. Organization Science Vol. 2 (1), pp. 125-134.
Sosa, M., Eppinger, S., Pich, M., McKendrick, D., Stout, S. (2002). “Factors that Influence Technical Communication in Distributed Product Development: An Empirical Study in the Telecommunications Industry”. IEEE Transactions on Engineering Management, Vol. 49, No. 1, pp. 45-58
Survey conducted between January – May, 2008. 20 participants from the following firms: Skidmore, Owings & Merrill LLP (SOM), Kohn Pedersen Fox Architects (KPF), Adrian Smith + Gordon Gill Architecture, Hellmuth, Obatta, Kassabaum (HOK), HWI Architects, Atkins, Arquitectonica.
Sutton, R. (2002). “Weird Ideas that Work - 11.5 practices for promoting, managing, and sustaining innovation”. The Free Press, New York, NY.
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Tapping, D., Shuker, T. (2002). “Value Stream Management”. Productivity Press.
Ulitskii, V. M., Shashkin, A. G., Shashkin, K. G., (2003). “Geotechnical Problems Associated with the Construction of High-rise Buildings”. Foreign Experience and Domestic Practice. Soil Mechanics and Foundation Engineering, Vol. 40, No.5. pp. 182.
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Woodbury, R., Burrow, A., (2006). “Whither Design Space?” Artificial Intelligence for Engineering Design, Analysis and Manufacturing. Vol. 20, pp. 63-82.
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Chapter 3: Design Scenarios – enabling requirements-driven parametric design spaces
Victor Gane, John Haymaker
1 Abstract Ideally, conceptual design processes in Architecture Engineering Construction (AEC) start with well-defined, multidisciplinary requirements used to generate large quantities of requirements-driven alternatives with well understood value to enable objective decision making. A quality design space is both well-defined and comprehensive as motivated by research in Design Theory or Systems Engineering. Concurrent Engineering and Requirements Engineering offer the means to gather requirements, Quality Function Deployment – to translate requirements into engineering characteristics, and Parametric Modeling – to generate large quantities of alternatives. Good designs in AEC are shaped by the interaction of decisions by many disciplines and today’s parametric solutions work well within their particular domain or discipline, but in reality a parameter in one discipline drives parameters in other disciplines. To build parametric design spaces, it is critical to integrate the process of determining project requirements, generating alternatives, and determining the performance and value of alternatives.
This paper presents a novel methodology called Design Scenarios (DS) intended for use in conceptual design of buildings. DS proposes to enable multi-stakeholder and multidisciplinary design teams to streamline the alternative generation and decision- making processes by providing a methodology for building and managing requirements driven design spaces with parametric Computer Aided Design (CAD) tools. DS consists of four interdependent models: (1) Requirements Model – stakeholders and designers explicitly define and prioritize context specific design requirements; (2) Scenarios Model (SM) – designers formally transform these requirements into actions necessary to achieve them, and determine the geometric and
52 Chapter 3: DS Methodology Victor Gane material parameters, interrelationships, and potential conflicts; (3) Parametric Process Model (PPM) – CAD experts build and represent the technical implementation of a SM in a parametric model to enable design teams to manage and communicate its logical construct and partially automate the generation of parametric models by linking PPMs to parametric CAD models; (4) Alternative Analysis Model – analyze and visually report performance back to the designers and stakeholders. This paper describes its implementation into a software prototype, and provides an example to illustrate how DS can potentially enable multidisciplinary teams to generate and communicate larger and better performing design spaces more efficiently than with traditional methods.
2 Introduction: the need for effective conceptual design processes Ideally, designers could create and analyze as many alternatives as possible. This entails the translation of requirements into possible alternatives and their evaluation for impact and value, or, in other words, the exploration of large design spaces and capturing the rationale used to build these spaces. The design process culminates in the selection of a building’s physical form. This process is complex and consists of constructing and assessing four spaces. The objective space consists of the constraints and goals determined by project stakeholders. The alternative space includes all possible design options describing geometric and/or material design decisions to be made, as well as the options chosen to generate specific alternatives. The impact space captures the performance of alternatives on goals and constraints. The value space shows how well the alternatives meet the stakeholder preferences on goals and supports the selection of successful alternatives (Clevenger & Haymaker, 2011). The construction and exploration of these spaces is difficult with today’s methods because the translation of multi-stakeholder requirements into specific parameters used to generate alternatives with a clearly understood value has not yet been formalized in AEC.
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Researchers such as Sutton (2002), Akin (2001), and Bock et. al. (2010) argue that successful designs emerge from exploring large design spaces, while Simon (1969) explains why designers’ bounded rationality forces them to narrow these spaces. Designers reduce the alternative space to address constraints such as the boundaries of the project site to find designs that maximize value to their goals. To assist them in generating alternatives, designers often adopt a scenario – a collection of structures and behaviors that represent the design intent (Baniassad and Clarke, 2004). In other words, a scenario is a set of selected constraints, which restrict the design space that needs to be further considered by capturing a set of related design decisions (Giesecke et. al. 2007). To Alexander (1977), a scenario is a “design pattern”, a “solution to a problem in a context” (Gamma et. al. 1995). In computer science a scenario (also called Theme, Pattern or Style) provides a “set of predefined subsystems, specifies the responsibilities of these subsystems, and includes rules and guidelines for organizing the relationships between them” (Buschmann et. al. 1996) and has an explicit representation (Giesecke et. al. 2007). In the AEC industry a scenario is a less formal construct generally communicated verbally or through hand sketches. Figure 3-1 illustrates an example of a traditional AEC scenario, in which the design architect with little or no participation of other design stakeholders might use the local context (e.g., Austrian Alps widely used for skiing) to implicitly constrain the design space (e.g., propose a building shape that draws its reference from skiing activity).
Figure 3-1: Example of AEC scenarios – concept sketches of a high-rise located in the Austrian Alps, which are derived from a ski pole (left) and a ski boot (right) (Gane and Haymaker, 2010).
Moran and Carroll (1996) argue that constructing effective design spaces requires explicitly communicating the design rationale but also that existing methods lack the
54 Chapter 3: DS Methodology Victor Gane structure required to efficiently capture and reuse knowledge, generate new insights, and develop consensus. Communicating design rationale is especially important for building design problems, which require “a multiplicity of views, each distinguished by particular interests and derived from an understanding of current problem solution techniques in the respective domain” (Stouffs, 2008).
Woodbury and Burrow (2006) and Goldschmidt (2006) identify two primary strategies to search through a design space – high breadth, low depth, which leads to multiple scenarios with a broad spread of options but little analysis, and low breadth, high depth, which leads to few scenarios with low spread of options but more comprehensive analysis.
Gane and Haymaker (2010) documented how traditional high-rise conceptual design process leads to a low breadth, low depth search strategy, in which the objective space is ill defined and the rationale used to create design spaces is poorly captured and communicated. Today’s methods do not ensure clarity of objectives and good practice depends entirely on the personal approach of individual designers. This does not make good design practices scalable, repeatable, and “automatable”. Today, with parametric methods designers can generate large alternative spaces using a high breadth, low depth (only geometry-based requirements can be assessed) search strategy. However, with traditional conceptual design methods, they are unable to leverage parametric methods to understand the impact and value spaces to select best alternatives. Design Theory or Systems Engineering researchers argue that to solve these shortcomings, design teams must address the following needs:
1. Capture and prioritize stakeholders’ and decision makers’ requirements (Chachere et. al 2011, Struck et. al. 2009); 2. Develop scenarios by decomposing requirements into actionable descriptions about ‘how’ to achieve them; 3. Translate the scenarios into qualitative and quantitative input and output parameters to describe physical and functional characteristics of a design (Lin et. al. 2009);
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4. Represent and manage geometry, dependencies, constraints, and CAD operations illustrating the parametric CAD model structure (Stouffs, 2008); 5. Manipulate and record parameter values to generate design alternatives (Struck et. al. 2009); 6. Visualize the alternatives; 7. Evaluate the alternatives (Zhang, et. al. 2010); 8. Compare evaluations and facilitate objective decision making (Colombo et. al. 2007).
Most of these needs are not new and several have been addressed by prior research. An integrated solution to enable effective use of parametric CAD is lacking, however. Specifically, this paper addresses the following primary question about these gaps:
. What is an ontology and method for designers and CAD experts to develop well defined and comprehensive alternative spaces that connect well defined and comprehensive objective, impact, and value spaces?
Figure 2 summarizes the main concepts introduced so far.
Figure 3-2: Summary of the concepts used to describe a design space and search methods.
To understand the quality of design spaces we need to measure how well-defined and comprehensive they are. In section 2 we introduce a framework for measuring the quality of design spaces. The framework is not the focus of this paper. However, the
56 Chapter 3: DS Methodology Victor Gane importance of this framework is to enable testing and understanding the impact of Design Scenarios and other proposed methodologies on the quality of design spaces. Our intuition is that as design teams generate and analyze scenarios explicitly, they can explore better defined design spaces that lead to better designs.
3 Framework for measuring the design space quality Since traditional conceptual design methods don’t ensure clarity of the objective, impact, and value spaces, but the ideal practice cannot live with such ambiguities, we define a framework to enable designers to assess the quality of parametric design spaces (Table 1). We use this framework in Gane et. al. (2011) to measure the impact of DS on an industry test case. Clevenger and Haymaker (2011) review and synthesize metrics for measuring design space quality, while Chachere and Haymaker (2011) review and synthesize methods for measuring design space clarity. In this paper we build on these methods to synthesize the following framework for assessing how well- defined and comprehensive the parametric design spaces are:
Table 3-1: Summary of topics covered by each guideline. Metric Definition Objective Space Size What is the number of project goals and constraints considered? Is the value function explicitly and broadly communicated? The clarity Objective Space is determined through documented statements describing stakeholders, Clarity goals, constraints, and preferences. Are the project goals and constraints determined by all key Objective Space stakeholders? A low quality denotes participation of <50% of Objective space Quality stakeholders; medium quality: 51-80%; high quality: 81-100%. Number of Scenarios What is the number of design scenarios considered? What is the total number of possible options comprising a scenario? Discrete versus continuous parameters are used to determine this metric Total Option Space by multiplying the constrained range of values of input parameters (e.g.,
Size building length between 30m and 40m) and their reasonable increment (e.g., 1m for building length). Generated Option What is the number of the generated design options for a scenario? Space Size What is the ratio of Total Options Space Size to Generated Options Space Size? A 1.0 ratio is ideal because it covers the complete Design
Alternativespace Options Space Quality Space for a given scenario. Statistical sampling of the space could also yield high quality option spaces, but as none of the cases used in our research have involved this, we reserve this for future research. Alternative Space Size What is the number of the generated design alternatives for a scenario? Alternative Space Are the scenarios, designers’ logic, and the parameters describing these
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Clarity scenarios clear? Are the structure of the parametric CAD model and the connection of CAD Model Clarity parameters in the CAD model to requirements clear? How many CAD models were generated for each design scenario? The CAD Model Quality target is to satisfy all geometry-based requirements with one parametric model, which denotes high quality. What is the number of formal model-based analyses performed to Impact Space Size determine the value of each alternative? Is the process and results of performing each analysis explicitly depicted Impact Space Clarity (i.e., repeatable)? What is the ratio of Impact Space Size to Objective Space Size? A 1.0
Impactspace Impact Space Quality ratio is ideal (i.e., for each requirement a formal analysis was performed). Out of the total number of generated alternatives, how many alternatives
Value Space Size have a clear value determined? The value is determined by designers understanding how well each Objective Space requirement is met. space Value Value Space Clarity Is the total value of each generated alternative explicitly defined?
Our aim with these metrics is not to achieve a full characterization of the conceptual design exploration process, but rather to provide a set of standard terms and measurements that support observation, comparison, and improvement of existing and novel processes.
In the remainder of this paper, we summarize existing research and identify gaps in existing concepts addressing the needs outlined in section 1. The major contribution of this paper is the Design Scenarios (DS) methodology, which we introduce in section 4. DS was developed as an integrated methodology to enable populating the framework and address the identified needs. We illustrate the application of DS through an illustrative example.
4 Points of Departure: design space exploration methods This section discusses research and gaps in addressing the identified needs. Concurrent Engineering integrates and parallelizes multidisciplinary tasks. Quality Function Deployment, Requirements Engineering, and Axiomatic Design provide formal frameworks for defining requirements and the roles these play in decision making (i.e., eliciting, analyzing, negotiating requirements), as well as prescribing a recommended course of action for achieving these requirements. Process Modeling languages represent and measure design spaces. Parametric Modeling efficiently
58 Chapter 3: DS Methodology Victor Gane generates alternatives spaces. While each of these methods address important subsets of the identified needs, gaps remain in how design requirements can be translated into parametric design spaces, which is the contribution of this paper.
4.1 Concurrent Engineering – integrate and parallelize tasks Several case studies show that poor definition or misunderstandings of requirements are major causes of system failure in software engineering (Rolland and Salinesi, 2005), mechanical engineering (Hsu and Woon, 1998), and in AEC (Kiviniemi et. al. 2004, Kam, 2005). Requirements-driven methods propose systematic approaches for screening and prioritizing design requirements.
Concurrent Engineering (CE) is a framework for achieving multidisciplinary objective spaces. CE addresses the limitations of traditional sequential design development methods by describing a set of technical, business, manufacturing planning, and design processes that are concurrently performed by elements of the manufacturing organization prior to committing to production (Miller, 1993). Cross-process integration is at the core of concurrent design and consists of a multidisciplinary team method and engineering of product lifecycle (Xiong, 2000). An already mature field, CE is at its third generation (Fukuda, 2007). The first generation addressed the limitations of sequential product development by noting the content of each design part, thus allowing independent parts to be processed in parallel. The second generation introduced the missing communication/negotiation among decision makers needed to determine the goals across the entire design process, and to relax some constraints. The current generation of CE helps determine the latest moment in the design process when binding decisions can be made. All four characteristics identified by Fukuda that describe successful application of CE apply to generating AEC design spaces: (1) high rate of design and process definition change (change rate); (2) high rate and short cycles of new design developments (speed); (3) designs with complex configurations that vary by client (complexity); (4) design processes that require multiple teams to produce a single product (multiple design teams).
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A key issue in concurrent engineering from a designer’s perspective is how to bridge the multitude of models required to support at various stages a complex design process. Although concurrent engineering is almost universally advocated today, it is hard to execute when large multidisciplinary projects are involved. CE requires a set of analytic tools and procedures to make its concepts operational (Yassine and Braha, 2003). In the context of this research, CE partially addresses need #1 by enabling designers to capture design requirements.
4.2 Quality Function Deployment – translate user needs into design characteristics Quality Function Deployment (QFD) is one of the methods comprising the field of systems engineering (Struck et. al. 2009) and an important point of departure for this research. QFD is a multi-phase design to production management model, which captures and prioritizes customer needs (objective space) and translates them into engineering design characteristics (alternative space). Vagueness in requirements eventually yields indifference to customer needs, while trivial characteristics make the team lose sight of the overall design and stifle creativity (Houser and Clausing, 1988). QFD avoids ambiguity in interpreting engineering characteristics through a systematic analysis of each characteristic (impact space). In QFD large-scale systems are decomposed by multidisciplinary teams into modules and evaluated against target requirements and cost by means of matrices (Takai and Ishii, 2006). A popular matrix example is “House of Quality”, which provides the means for inter-functional planning and communication (Houser and Clausing, 1988). The QFD process starts with the customer requirements, continues with ‘functions’ required by the products or services to be developed, and ends with identifying the means for optimal ‘deployment’ of available resources to produce the desired products or services. Research shows that the competence of engineering designers is related to their ability to consider design constraints (Colombo et. al. 2007). Traditional QFD tools are enhanced by assessment methods that include constraints (Leary and Burvill, 2007).
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QFD helps design teams determine the objective, alternative, and impact spaces. It enables understanding stakeholder requirements, engineering characteristics, and their relationships and target values, and has been extended to help guide designers in the translation of requirements into feasible design options (Chen and Pai, 2005). However, QFD does not enable translating requirements into parametric CAD models. In the AEC industry, PREMISS (Kiviniemi et. al. 2004), Decision Dashboard (Kam, 2005), and MACDADI (Haymaker and Chachere, 2007), are other examples of methodologies for eliciting requirements and relating them to building design alternatives. Similar to QFD, these methodologies also lack the means to reliably identify and relate parameters to drive geometric design spaces from requirements models. In the context of this research, QFD satisfies need #1 by enabling designers to capture and prioritize design requirements, partially satisfies need #2 by helping decompose requirements for a single scenario into actionable descriptions, and satisfies need #7 by evaluating design options against customer requirements.
4.3 Requirements Engineering – determine and manage requirements Requirements Engineering (RE) provides another method to build objective and alternative spaces by formalizing the requirements gathering and specification process represented in the form of a checklist of requirements. Originating in systems and software engineering (Laplante, 2007), RE overcomes the drawbacks of traditional software development methods, in which the developed systems are often technically good but unable to appropriately respond to user needs (Rolland and Salinesi, 2005). RE states why a system is needed based on current and foreseen conditions, what requirements the system will satisfy, and how the system is to be constructed (Ross and Schoman, 1977). An expanded RE definition is concerned with making such goals operational by transforming them into services and constraints, and assigning responsibilities to agents, including humans, devices, and software (Lamsweerde, 2000). Reasoning with goals can also help resolve conflicts among stakeholders. For example, it is important to capture the fact that one goal can prevent another from being satisfied. AND/OR graphs are used to capture goal refinement links (Lamsweerde, 2000). An OR node represents a choice between possible
61 Chapter 3: DS Methodology Victor Gane decompositions while an AND node represents a required decomposition. A conflict- link between two goals is introduced when the satisfaction of one goal may prevent another from being satisfied.
In the context of this research, RE partially addresses need #1 by enabling designers to capture but not prioritize design requirements; addresses need #2 by decomposing requirements into actionable descriptions of how to achieve them; partially addresses need #4 by representing and managing dependencies and constraints, but not geometry and CAD operations.
4.4 Axiomatic Design – generate requirements and enable parameters Axiomatic Design (AD) provides a theoretical framework to help reduce the complexity of the design space and improve decision making at all levels (Suh, 1998). AD represents design in terms of four domains: (1) Customer Domain identifies end user needs and design specifications (objective space); (2) Functional Domain identifies functional requirements needed to satisfy customer needs; (3) Physical Domain identifies designs satisfying the functional requirements (alternative space), and (4) Process Domain identifies the processes needed to determine design parameters (Suh, 1995). AD defines design as a process of mapping designers’ requirements from the functional to the physical domain. Suh defines Functional Requirements (FRs) as the minimum number of independent requirements that characterize a design solution. AD stipulates two fundamental axioms that govern the design process. The Independence Axiom states that the independence of FRs has to be always maintained. In other words, in case of design problems with multiple FRs, a good design solution is made of design parameters (DP) that result in the independence of the FRs from each other. The Information Axiom states that information content of the design must be minimized.
The output of AD is a design matrix used to determine relationships between DPs and associated FRs. The shape of the matrix is used to distinguish between good and bad designs. Uncoupled designs are considered ideal because adjustments to the FRs are the easiest to make. A decoupled design is less desirable given the increased
62 Chapter 3: DS Methodology Victor Gane complexity in relationships between a DP and several FRs. A typical AEC design problem is often a decoupled design. A coupled design is the worst and generally calls for the selection process of DPs to be repeated. In the context of this research, AD partially addresses need #1 by helping to capture but not prioritize design requirements; partially addresses need #3 by enabling designers to translate requirements into design parameters without distinguishing between input and output parameters; and partially addresses need #4 by representing dependencies between requirements and parameters, but not constraints, geometry, and CAD operations.
4.5 Process modeling – represent and measure design spaces Building a shared ontology is critical for increasing the effectiveness of multidisciplinary teams (Xexe et. al. 2005). Process modeling is a medium for building shared ontologies to help organizations plan, measure, compare, and adopt well-defined processes. Generally, there are three applications for process models: a) descriptive for describing what happens during a process; b) prescriptive for describing a desired process; c) explanatory for describing the rationale of a process (Rolland and Pernici, 1998). Some languages help system developers define software and databases. IDEF (Integration DEFinition) is a family of modeling languages from systems engineering covering issues such as functional modeling, data acquisition, and simulation. Unified Modeling Language (UML), also from systems engineering, consists of structure and behavior diagrams to describe a system’s functional requirements, structure, procedural flow of class objects, etc. (Fowler, 2004). Froese (1996) describes many of the core and application process models for AEC, including IRMA – Information Reference Model for AEC, BPM - Building Project Model, ICON – Information / Integration for Construction, GRM – Generic Reference Model. GTPPM (Lee et. al. 2007) integrates multiple use-cases with differing data requirements to define databases that facilitate collaboration among design teams. Other languages are intended for use directly by design teams. For example, Value Stream Mapping (Tapping and Shuker, 2002) helps teams illustrate the flow of activities, and information that produce value in a given process, while Narratives (Haymaker et. al. 2004) help teams model and manage the information and the
63 Chapter 3: DS Methodology Victor Gane sources, nature, and status of the dependencies between information in a process. These existing process modeling methods lack a representation formalism for communicating the structure of parametric CAD models (GTPPM being the exception), as well as their relationships to the requirements they address, and performance they achieve. In the context of this research, process modeling partially addresses need #1 by capturing but not prioritizing design requirements; partially addresses need #2 by showing actions but not decomposing requirements into actions.
4.6 Parametric modeling – generate alternative spaces The development of procedures for generating design alternatives is an active research area. For example, shape grammars (Knight, 2000) are a class of production systems used to generate geometric alternatives based on a set of transformation rules. Graph grammars consist of a set of rules that illustrate ways of constructing a design product or process as a graph represented by nodes denoting objects and arrows denoting relations between objects (Rozenberg, 1997). Multidisciplinary Design Optimization methods guide generative methods to automatically select optimal designs (AIAA, 1991). Others (Kelley, 2006, Sutton, 2002) adopt more human-centric approaches, regarding the concept of “brainstorming” as the backbone of creative thinking. A balance is needed between both strategies – breadth (i.e., brainstorming) for initial idea generation and depth (i.e., geometry adjustment) for refining alternatives. Parametric modeling can support building design spaces with great breadth (multiple geometric alternatives) and partial depth (analysis of geometry-based requirements only).
Parametric CAD is used to create and manage geometric alternative spaces. Also called constraint or feature based, associative modeling, parametric modeling can enable designers to shift from creators of single designs to designers of systems of inputs and outputs that generate design spaces. The concept of “features” encapsulates generic shapes or characteristics of a product with which designers can associate certain attributes and knowledge useful for reasoning about that product (Shah and Mäntylä, 1995). To design parametrically means to design a constrained system that
64 Chapter 3: DS Methodology Victor Gane sets up a design space that can be explored through the variations of parameters (Kilian, 2004). Using parametric models, designers can create an infinite number of objects, geometric manifestations of a previously articulated schema of variable dimensional, relational or operative dependencies (Koralevic, 2003). Designing with multiple constraints without an efficient constraint management system is a daunting task. An example of a constraint management methodology are the design sheets, in which design models are represented as constraints between variables in the form of nonlinear algebraic equations organized into bipartite graphs and constraint networks (Reddy et. al. 1996). Using design sheets to define parametric models, however, is not intuitive given the overwhelming number of constraints that need to be described at the schema level and the inability to visualize geometry, a capability that AEC designers need. Therefore parametric systems using geometric constraint programming to graphically impose constraints helps designers solve the relevant nonlinear equations without having to explicitly formulate them (Kinzel et. al. 2007). Existing methods such as Building Object Behavior (BOB) (Lee, et al., 2006), and software solutions such as Bentley’s Generative Components or McNeel’s Grasshopper for Rhinoceros (http://wiki.mcneel.com/labs/explicithistory/home) address parts of the needs by helping designers define the structure of the parametric models (need #4), manage parameter values to generate alternatives (need #5), visualize alternatives (need #6), and geometrically evaluate alternatives with output parameters (partially need #7). However, parametric modeling needs a formal method for deriving constraints and parameters from requirements, and for relating the resulting alternatives to analyses performed outside of the parametric model.
In summary, each point of departure (POD) helps address parts of the identified needs. However, an integrated solution is still missing, including an ontology for building multidisciplinary AEC alternative spaces and systematic transfer of design requirements from the objective space to the alternative, impact and value spaces. Figure 3-3 graphically summarizes the relationship of the PODs to the needs we identified.
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Figure 3-3: Summary of how PODs satisfy the identified needs. This paper focuses on creating an ontology for building multidisciplinary AEC alternative spaces and a method to translate design requirements from the objective space to the alternative, impact, and value spaces.
5 Design Scenarios - methodology description The Design Scenarios (DS) methodology was developed as an integrated solution to the identified needs. It enables design teams to develop and capture parametric scenarios in a relational network connecting objective, alternative, impact, and value spaces. DS builds models for each of the four spaces. However, to enable the translation of requirements into alternatives, we divided the alternative space into two subspaces: Alternative Space Logic, where designers capture their logic for addressing requirements, and Alternative Space Geometry, where CAD experts use designers’ logic to build requirements-driven parametric models. Because each space requires the participation of various roles, we introduce the concept of an Enabler (e.g., Architect, CAD expert) who uses a Method (e.g., Create objective) to generate an Element (e.g., Goal, parameter). Each model contains various enablers who are either humans or the computer. DS has a total of 30 methods and 29 elements. The selection of methods and elements varies for each model (see Chapter 1).
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Figure 3-4 illustrates the DS process, which after the project administrator completes the project setup, starts with building the objective space in the Requirements Model (RM). The RM enablers are the stakeholders and designers, who concurrently create and prioritize project constraints and goals. The process continues with constructing the logical alternative space in the Scenarios Model (SM). The SM enablers are the computer and the designers, who concurrently decompose the requirements transferred by the computer from the RM into key geometric and/or material parameters and relationships. The computer then transfers the SM parameters into the geometric alternative space in the Parametric Process Model (PPM), where the CAD experts define the structure of dependencies between parameters, geometric constraints, CAD operations, and geometry. CAD experts use the PPM to construct the parametric model and generate design alternatives. The process continues with building the impact space in the Alternatives Analysis Model (AAM). The AAM enablers are the designers, who determine the performance of alternatives given the RM requirements. The process is finalized with building the value space, which in DS is also completed in the AAM. The enabler is the computer, which determines the value of each analyzed alternative in relation to the goal targets and preferences, thus enabling design teams to make objective decisions.
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Figure 3-4: Design Scenarios methodology process description. The Objective Space is captured in the Requirements Model; the Logical Alternative Space in the Scenarios Model; the Geometric Alternative Space in the Parametric Process Model; the Impact and Value Spaces in the Alternatives Analysis Model.
We implemented the Design Scenarios methodology into a web-based software prototype with the same name developed in Java and Ruby on Rails, and supported by a MySQL database management system. In addition to the four DS models comprising the methodology and represented in either tabular or process model format, the software contains a Project Administration interface used to create new projects and add users, and a Project Setup interface used to create projects roles, assign users to these roles, determine access privileges to each model, and assign stakeholder influence weights. A description of these two modules can be found at www.designscenarios.com.
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5.1 Requirements Model (RM) Methodology description
Building on a Concurrent Engineering framework, the RM parallelizes the requirement definition process of all project stakeholders (e.g., client, architect, mechanical engineer, structural engineer) to enable collective understanding of each others’ requirements. MACDADI (Haymaker et. al. 2006) and Requirements Engineering (Lamsweerde 2000) provided the foundation for the RM elements. An RM is a tabular model built by project stakeholders and designers who concurrently generate constraints and goals (two out of four RM elements). Constraints must be satisfied, while goals can be traded off against each other when finding an optimal design. When all stakeholders finish generating requirements, each stakeholder distributes 100 points over the identified goals to represent individual preference (third RM element). Many rating methods can be used, however this technique was chosen for the first implementation of the DS methodology because of its simplicity and ease of implementation. When all stakeholders finish assigning preferences, the computer generates a cumulative goal importance score for each goal, the sum of which is normalized to 100 points (fourth RM element).
Software Implementation
In Table 3-2, we describe the RM in detail. The left column gives the visual representation and definition of each element of the RM, while the right column uses the EXPRESS data model (ISO 10303-11, 1994) to describe each concept as implemented in the software prototype. Some user and system-defined inputs use free- form string data types that enable users to represent either text or values.
Weighting stakeholders is also relevant and has been implemented in the Project Setup interface of the software prototype. Requirements can be qualitative or quantitative, and can range from those defined by stakeholders (e.g., client: building use, space efficiency; planning department: shadows, density of development), to those established by the designers (e.g., architect: design language; mechanical engineer: daylight factor, energy comfort).
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Table 3-2: RM graphical notation, definitions, and data schema in the Design Scenarios software prototype. Term notation / definition Schema description User inputs: ENTITY Constraint discipline name: ARRAY OF STRING; discipline abbreviation: ARRAY OF Constraint – restriction on the STRING; quantitative or qualitative value of a constraint name: ARRAY OF STRING; design parameter. value: ARRAY OF STRING;(free-form) unit: ARRAY OF STRING; END_ENTITY; User inputs: ENTITY Goal discipline name: ARRAY OF STRING; discipline abbreviation: ARRAY OF STRING; Goal – quantifiable or qualitative value name: ARRAY OF STRING; of a design parameter that is desirable to target value: ARRAY OF STRING; be achieved. (free-form) unit: ARRAY OF STRING; END_ENTITY; System inputs: ENTITY Goal importance goal percentage value: ARRAY OF STRING; Goal preference – a value expressing User inputs: the relative importance of a goal to a goal relative value: ARRAY OF stakeholder STRING; (free-form) END_ENTITY; System inputs: ENTITY Goal cumulative importance goal cumulative value: ARRAY OF Cumulative goal importance – a value STRING; END_ENTITY; expressing the sum of importance of a goal to all stakeholders
The major benefit of building a RM is the process of determining a comprehensive set of multidisciplinary requirements, which can help eliminate the non-productive ambiguity in current early building design decision making practice. Building a RM does not require significant additional skills, time investment, or the physical presence of participants. The identified constraints and prioritized goals serve as inputs to the Scenarios Model. The RM also provides the formal value function for determining the value of design alternatives in the AAM and assists in decision-making. The novel features introduced in the RM is the transfer of goals and constraints from the
70 Chapter 3: DS Methodology Victor Gane objective to the logical alternative space and transfer of stakeholder preferences from the objective to impact space.
The RM addresses need #1, and makes populating the Objective Space Size, Clarity, and Quality metrics in the proposed framework possible.
5.2 Scenarios Model (SM) Methodology description
To enable building well-defined alternative space logic and thus address needs #2 and #3, designers need to capture and communicate how they intend to address requirements parametrically. A prescriptive process model offers the means to do that. The SM is a prescriptive process model that builds on the scenario concept from Requirements Engineering. The first author’s knowledge of the concepts that design teams currently use implicitly in the industry as well research from Requirements Engineering (e.g., First Order Logic) provided the foundation for the SM elements. The enablers in the SM are designers, who begin the process of building the SM with the RM-established requirements. Building on the Concurrent Engineering framework, multiple designers concurrently decompose the same requirement into four inter- related levels of decision elements: action items strategies parameters parameter constraints.
An action item is an actionable description of how to achieve a requirement. An action is generally addressed through multiple strategies - processes required to achieve an action. Both actions and strategies are decomposed into parameters - variables denoting properties impacting a design requirement. The last decision level is the parameter constraint - a fixed value or upper and lower limit of values and an increment that a parameter might be required to be within. When designers create multiple same-level decision elements (e.g., three action items for the same constraint), they specify how such decision elements relate to each other. AND/OR graphs (Lamsweerde, 2001) widely used in Requirements Engineering can efficiently describe simpler relationships but are not efficient in more complex cases since this would lead to duplication of SM elements and result in model scalability issues.
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Instead, in SM designers use First Order Logic (Andrews, 2002) to account for the more challenging logical conditions (Figure 3-5). First Order Logic formalisms generally describe a relation of inclusion (Stouffs, 2008) represented in the SM as AND, OR, XOR logical gateways.
Figure 3-5: First Order logic implemented in the SM. R1, R2 are the requirement nodes generated in the SM; A, B, C, D represent the action, strategy, or parameter nodes in SM.
Some actions may conflict with requirements. Designers represent potential conflicts in SM that they identify either experimentally or based on expertise and intuition. For example, in addressing the goal of minimizing a building design construction cost, an experienced designer will see a potential conflict when choosing among several strategies for exterior wall systems that vary in cost. In such cases, designers draw a potential conflict arrow element from action or strategy decision element to the affected requirement(s). Identifying potential conflicts helps reduce the design space size by eliminating or mitigating conflicting actions and the dependent strategies, parameters and parameter constraints. Designers need to ensure that they provide enough decision nodes that do not result in conflicts to avoid prohibiting the development of a design.
Designers distinguish between input and output parameters by drawing a parameter dependency arrow between two parameters. Designers assign concrete values to input parameters and build relationships (generally described as algebraic expressions in AEC design problems) driving the output parameter values. Output parameters have parameter dependency arrows pointing from input parameter nodes. Parameter constraints elements prescribe a range of values for the parent input parameter node and include the upper and lower extremes and the parameter increment.
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Software Implementation
In Table 3-3 we describe the SM ontology, which includes several decision node types, and logical and process relationships among nodes that enable design stakeholders to generate and communicate multiple scenarios for the same design project. The system transfers constraints and goals between the RM and SM to serve as a starting point for designers to explicitly describe their logic to address requirements. To enable designers to determine parts of the SM that might be out of date, the mapping process captures any modifications in the RM as time stamps, which are reflected in the equivalent requirements nodes in the SM. The system enables designers to indicate the choice of scenarios by setting the decision node status: (a) asserted – indicates confident choice; (b) retracted – indicates a rejected choice. If designers retract a decision node, all other dependent decision nodes are retracted as well; (c) assumed – indicates uncertainty and a choice that might need revision. A retracted status propagates both down and upstream through SM arrows. The SM also enables designers to quantify the down and upstream dependencies for any node, and the number of requirements impacted by each action item to help determine high impact parameters.
Table 3-3: SM graphical notation, definitions, and data schema and in the Design Scenarios software. Term notation / Schema description definition System inputs: ENTITY Constraint name: STRING; value: STRING; (free-form) unit: STRING; Restriction on the created by: USER; quantitative or last updated: DATE; node color: BLUE; qualitative value of a User inputs: design parameter status: (ONEOF (Asserted, Retracted, Assumed)); END_ENTITY; System inputs: ENTITY Goal name: STRING; Quantifiable or value: STRING; (free-form) unit: STRING; qualitative value of a created by: USER; design parameter that is last updated: DATE; desirable to be achieved node color: BLUE;
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User inputs: status: (ONEOF (Asserted, Retracted, Assumed)); END_ENTITY; System inputs: ENTITY Action item created by: USER; last updated: DATE; node color: GREEN; An actionable upstream dependencies: STRING; description of how to downstream dependencies: STRING; User inputs: achieve each ENTITY Action item requirement description: STRING; status: (ONEOF (Asserted, Retracted, Assumed)); END_ENTITY; System inputs: created by: USER; last updated: DATE; node color: RED; upstream dependencies: STRING; downstream dependencies: STRING; A process required to User inputs: achieve an action item ENTITY Strategy description: STRING; status: (ONEOF (Asserted, Retracted, Assumed)); END_ENTITY; System inputs: created by: USER; last updated: DATE; node color: YELLOW; upstream dependencies: STRING; downstream dependencies: STRING; User inputs: A variable denoting ENTITY Parameter properties that impact a name: STRING; design requirement value: STRING; (free-form) type: STRING; status: (ONEOF (Asserted, Retracted, Assumed)); END_ENTITY; System inputs: created by: USER; last updated: DATE; node color: YELLOW; A fixed value or range of upstream dependencies: STRING; downstream dependencies: STRING; values (shown as lower User inputs: and upper limit nodes) ENTITY Parameter constraint that a parameter might name: STRING; be required to be within value: STRING; (free-form) END_ENTITY; System inputs: upstream dependencies: STRING; downstream dependencies: STRING; User inputs: Logical gates describing ENTITY Logic gate relationships between function: (ONEOF (AND, OR, XOR)); actions, strategies, and END_ENTITY; parameters.
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AND – all on, OR – at least one on, XOR – at least one on and one off
Enabling operation User inputs: arrow – denotes SM ENTITY DS arrow process operation. type: (ONEOF (Enabling operation, Parameter dependency, Potential conflict)); Parameter dependency connect: (Constraint AND Logical gate) OR arrow – distinguishes (Goal AND Logical gate) OR (Logical gate AND Action item) OR between intput and (Logical gate AND Strategy) OR output parameters. (Logical gate AND Parameter) OR (Parameter AND Parameter constraint)OR (Action item AND Parameter) OR Potential conflict arrow (Parameter AND Parameter) OR –illustrates conflicts (Action item AND Constraint) OR between action items or (Action item AND Goal) strategies and END_ENTITY; constraints/goals Inter-model transfer – System inputs: connects and establishes ENTITY Inter-model transfer type: (Requirements Model to Scenarios a dependency of same Model); requirement used in the connect:(ONEOF (Constraint AND Constraint) OR RM and SM (Goal AND Goal); END_ENTITY;
The novel features introduced in the SM include an ontology for building the logical alternative spaces and the process of transferring SM parameters to the geometric alternative space. The SM enables populating the Total Option Space Size and Option Space Quality metrics in the proposed framework for measuring design space clarity and quality.
5.3 Parametric Process Model (PPM) Methodology description
To enable building well-defined and comprehensive geometric alternative spaces, CAD experts need to determine, manage, and communicate how designers’ scenarios, logic, and parameters are linked to geometry inside parametric models. This also entails addressing the CAD model technical issues, such as efficient navigation and management of large models. A process model offers the means to do that. The PPM is both a descriptive and prescriptive process model that enables CAD experts to generate and communicate with 18 methods and 12 elements the logical construct and
75 Chapter 3: DS Methodology Victor Gane technical implementation of a chosen SM scenario in a parametric CAD model used to generate and search through alternative spaces (need #4). The first author’s knowledge of the concepts that CAD experts use to build parametric models provided the foundation for the PPM elements.
The enablers in the PPM are the CAD experts, who connect the well-defined and comprehensive designers’ logic to computable parametric models. The CAD experts begin the process of building the PPM with the SM-established parameters. CAD experts refer back to the SM to understand the designers’ scenario(s), logic, and decision choices and select the appropriate geometric elements (e.g., line, arc) for the identified scenario to which they link the corresponding input and output parameters. To enable predictable interaction with the parametric model, CAD experts constrain the geometric elements (e.g., tangency relation between two arcs). To create new geometry, CAD experts use CAD operations (e.g., extrude) and Reference elements (e.g., XY plane) to establish the CAD operation direction. CAD experts use the completed PPM to construct the parametric model. CAD experts use the resulting CAD model to search through the alternative space delimited by the SM scenario (e.g., round building shape), evaluate the alternatives’ performance against requirements that can be assessed geometrically through output parameters (e.g., building area), and extract the design alternatives that satisfy the geometry-based requirements for further analysis in discipline-specific tools (e.g., daylight, thermal comfort).
Software Implementation
In Table 3-4 we describe the PPM ontology, which includes several node types, and logical and process relationships among nodes. A PPM contains two levels of information abstraction. At the component-level the model illustrates how the CAD model is decomposed into components (e.g., floor plates, exterior wall) and their dependencies (e.g., exterior wall is dependent on floor plates). This is especially important when working with large CAD models that can become overwhelming to manage if no decomposition is pursued. At the geometry-level, a PPM describes the composition of elements in each component. All nodes contain system-generated type- dependent attributes (e.g., Extrude CAD operation attributes include a Profile,
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Direction, and a Length value) that serve as prerequisite inputs for generating a node. Groups of geometry-level PPM nodes can be associated with or disassociated from a component. A node from one group can be cross-associated with another group. To help efficiently navigate large PPM models, CAD experts can “focus” a component to highlight the geometry-level grouped nodes that describe its composition and fade the rest.
The system transfers input and output parameters between the SM and the geometry- level PPM to help link requirements-driven parameters to CAD models. The mapped parameter nodes serve as inputs to geometry nodes. CAD experts choose which geometry node types to use based on the actions and strategies captured by designers in the SM (e.g., Action → Generate building footprint; Strategies → Rectangular OR Round, which will lead to choosing either a Line or Arc attribute in the geometric element node and link either a Length or Radius parameter describing each strategy).
The PPM enables extracting input and output parameter nodes as tabular data and automates their generation in such parametric CAD tools as CATIA or Digital Project. The CAD expert manually builds the other PPM nodes in CAD following the PPM prescribed structure and dependencies.
The novel features introduced in the PPM include an ontology for building the geometric alternative spaces and the process of transferring PPM parameters to the impact space. The PPM enables populating the CAD Model Clarity and Quality metrics in the proposed framework for measuring design space quality.
Table 3-4: PPM ontology and graphical notation. Term notation / Schema description definition System inputs: node color: GREEN; User inputs: ENTITY Component An information name: STRING; container describing a label: STRING; (free form) component-level focus: (ONEOF (On, Off)); decomposition of the END_ENTITY; CAD model.
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System inputs: set of attributes: SET OF STRINGS; node color: BLUE; User inputs: A predetermined ENTITY Geometric element geometric primitive ABSTRACT SUPERTYPE OF (ONEOF (Point, Line, Circle, used to create the Spline Polyline, Arc, Ellipse)); geometric name: STRING; custom attribute: STRING; (free-form) representation of the component association: (ONEOF (Associate, intended design Disassociate); END_ENTITY; System inputs: identical to geometric element node type except node outline (dotted). User inputs: identical to geometric element node type. A geometric element used to construct the design intent but not explicitly featured in the final design representation System inputs: set of attributes: SET OF STRINGS; node color: BROWN; User inputs: ENTITY Reference element ABSTRACT SUPERTYPE OF (ONEOF (Offset from plane, Parallel through point, Angle normal to plane, Through A plane of reference three points, Through two lines, Through point and used to determine the line, Through planar curve, Normal to curve, Tangent to orientation of the surface)); geometric elements name: STRING; component association: (ONEOF (Associate, Disassociate); END_ENTITY; System inputs: set of attributes: SET OF STRINGS; node color: GREEN; node shape: DIAMOND; User inputs: ENTITY CAD operation ABSTRACT SUPERTYPE OF (ONEOF (Project, Intersect, An action performed on Extrude, Revolve, Offset, Fill, Loft, Blend, Join, Split, Translate, Rotate, Symmetry, Scale)); geometric element(s) in custom attribute: STRING; a CAD model name: STRING; component association: (ONEOF (Associate, Disassociate); END_ENTITY; System inputs: name: STRING;(if Parameter created in SM) value: STRING; (free-form) Node color: YELLOW; A user-controlled User inputs: parameter, which ENTITY Global Input parameter affects multiple name: STRING;(if New parameter) geometric elements custom attribute: STRING;(if New parameter)
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within a CAD model component association: (ONEOF (Associate, Disassociate); END_ENTITY; User inputs: identical to global input parameter. System inputs: identical to global input parameter node except node outline (dashed). A user-controlled parameter, which affects a single geometric element within a CAD model System inputs: identical to global input parameter except node outline (dotted). User inputs: identical to global input parameter. An input parameter with a constrained value System inputs: name: STRING;(if Parameter created in SM) value: STRING; (free-form)(if created in SM) node color: YELLOW; node shape: OVAL; User inputs: ENTITY Output parameter A parameter whose name: STRING;(if New parameter) value is determined value: input parameter 1 AND STRING (free-form) AND formulaically input parameter n; (if New parameter) component association: (ONEOF (Associate, Disassociate); END_ENTITY; System inputs: Node color: RED; User inputs:
ENTITY Geometric constraint ABSTRACT SUPERTYPE OF (ONEOF (Fixed, Horizontal, A constant, non- Vertical, Coincidence, Concentric, Perpendicular, numerical relationship Tangent, Parallel)); between geometric set of attributes: SET OF STRINGS; name: STRING; elements component association: (ONEOF (Associate, Disassociate); END_ENTITY;
System inputs: Component start component name: STRING; sequencing – illustrates end component name: STRING; the sequence of User inputs: construction process of ENTITY PP Component arrow connect: Component AND Component; components END_ENTITY; System inputs: Input / output start node name: STRING; dependency – end node name: STRING; illustrates information User inputs:
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dependency applicable ENTITY PP arrow to all but component connect: (Geometric element AND Constraint) OR nodes (Reference element AND Constraint) OR (Reference element AND CAD operation) OR (Reference element AND Geom. element) OR (Input parameter AND Geom. element) OR (Output parameter AND Geom. element) OR (Input parameter AND Output parameter) OR (Input parameter AND CAD operation) OR (Output parameter AND CAD operation) OR (Input parameter AND Output parameter) END_ENTITY; Inter-model transfer – System inputs: connects and ENTITY Inter-model transfer type: Scenarios Model to Parametric Process establishes a Model; dependency of the same connect (ONEOF (Input Parameter AND Input parameter in the SM Parameter) OR (Output parameter AND Output and PPM parameter); END_ENTITY;
5.4 Alternatives Analysis Model (AAM) Methodology description
The AAM is a tabular model developed by the designers to evaluate how each alternative analyzed in parametric CAD or discipline-specific tools ranks in relation to the goals identified in the RM, thus enabling building the impact and value spaces and addressing need #8. MACDADI (Haymaker et. al. 2006) provided the foundation for the AAM elements.
The enablers in the AAM are the designers. DS method asks designers to perform a formal analysis (e.g., daylight) for every parametrically generated alternative and determine a well-defined impact score given the RM constraints and goals. A major benefit of DS method is that designers are ensured to perform analysis only on alternatives that satisfy all the geometry-based requirements. Benchmark-based scoring enables designers to determine and compare the impact of each alternative’s performance against the RM goals’ targets, calculated as a percentage of the goal target value. Designers assign scores measured in percentage points to each alternative based on low and high benchmarks (e.g., high benchmark: minimize cost to $80,000, low benchmark: minimize to $100,000). If an alternative achieves a goal, it receives a 100% score. If it exceeds it (e.g., $70,000, it receives the percentage scored above the
80 Chapter 3: DS Methodology Victor Gane high benchmark – 112.5%, etc.) Benchmark values vary for each requirement and are determined in the RM by the stakeholder who proposes the requirement.
To determine the final multidisciplinary performance value of each alternative, the DS method multiplies the impact score for each goal with the appropriate goal importance score transferred from the RM and sums these into a final value score.
Software Implementation
In Table 3-5 we describe the AAM ontology. The AAM consists of user-generated and system-generated inputs. The former includes goal impact scores and parameter values for each analyzed alternative. The latter includes alternatives’ value scores for each goal and the total value score for each alternative.
The system transfers the goals between the RM and AAM, and the input and output parameters between the PPM and AAM as shown with the horizontal arrows in Fig. 4 and with the last definition in Table 3-5. Users input the alternatives that were analyzed and need to be scored and upload the alternatives’ geometric previews.
The AAM offers multidisciplinary design teams a formal unifying structure and communication tool for describing and comparing the quantitative and qualitative analyses of design alternatives to enable improved decision-making. The AAM enables populating the Value Space Size and Clarity metrics in the proposed framework.
Table 3-5: AAM ontology and graphical notation. Term notation / definition Schema description System inputs: goal name: ARRAY OF STRING; User inputs: ENTITY Impact score alternative score: ARRAY OF STRING; (free- form) Alternative impact score alternative preview: ARRAY OF IMAGE; determines the percentage of END_ENTITY; the goal target value. System inputs: ENTITY Design parameter Design parameter illustrates parameter name: ARRAY OF STRING; the parameter(s) and the parameter alternative value: ARRAY OF value(s) used in generating a STRING; (free-form) END_ENTITY;
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design alternative System inputs: Alternative illustrates the ENTITY Alternative alternative name: ARRAY OF STRING; design alternative(s) that satisfy constraints and END_ENTITY; selected to be analyzed against goal target values System inputs: ENTITY Value score goal name: ARRAY OF STRING; Alternative value illustrates goal importance: ARRAY OF STRING; (free- the score calculated by form) multiplying the alternative goal alternative value score: STRING;(free- form) impact score and goal alternative value score: ARRAY OF STRING; importance. Value score (free form)
illustrates the sum of END_ENTITY; alternative’s impact scores for all goals System inputs: ENTITY Inter-model transfer Inter-model transfer – type: (ONEOF (Requirements Model to connects and establishes a Alternatives dependency of the same goal Analysis)OR (Parametric Process Model to or parameter in RM, PPM and Alternatives Analysis Model); connect (ONEOF (Goal AND Goal) OR (Input AAM models Parameter AND Input Parameter) OR (Output parameter AND Output parameter); END_ENTITY;
5.5 Illustrative Example This section explains the application of DS through a simple, hypothetical example that has three stakeholders – client (a University), architect, and mechanical engineer. Figure 3-6 illustrates the site between the four central planters in the University Quad, where a new teaching space is to be designed.
Figure 3-6: The site for a teaching space.
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5.5.1 Requirements Model
The design process begins with the analysis of the site opportunities and constraints. The client creates a constraint in the RM – minimum usable area of 3,000 square feet. The site helps the architect identify an additional constraint – the buildable area set back of 10 feet from the four adjacent planters to allow circulation around the building. The mechanical engineer adds the goal to maximize the use of daylight to an average of 500 lux required for a teaching space and thus minimize the use of electric lighting. The client adds the goal to minimize the construction cost to below $100,000.
Once the requirements are synthesized and accepted by all parties, stakeholders and designers individually rank each goal according to their preference. All stakeholders are weighted equally in this example. Figure 3-7 illustrates the client’s preference for minimizing construction cost by assigning a 60% relative importance value. When stakeholders complete assigning importance to goals, the system generates a cumulative importance percentage graph. By comparing weighted characteristics of goals, design teams can set priorities. Figure 3-7 shows that maximizing use of daylight emerged as the prevailing goal with a 65% cumulative percentage score. The RM helps clarify what the project requirements are, who generated them, and how important are they to the project stakeholders in an integrated, concurrently generated model. The concept of requirements if not novel, however prior to developing the RM, design teams could not ensure that requirements are well-defined by all stakeholders.
Figure 3-7: The Requirements Model captures the stakeholders’ constraints, goals, and preferences for goals. Stakeholders distribute a percentage of preference (totaling 100%) to each identified goal.
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5.5.2 Scenarios Model
Establishing a scenario enables design stakeholders (architect and mechanical engineer) to determine the alternative space extremes based on the identified set of requirements. The architect suggests investigating two scenarios – one single large teaching space configuration and two smaller ones, both with perimeter windows to address the daylight goal. This decision clarifies the range of geometric variations – from a square to a rectangle (Figure 3-8).
Figure 3-8: The architect suggests two scenarios (square and rectangular classroom) that enables determine the desired range of geometric variations.
Figure 3-9 shows the case study SM model. The model starts with the goal and constraints nodes mapped from the RM and is concurrently decomposed by the design stakeholders into action, strategy, parameter, and parameter constraint decision nodes. The design space extremes are explicitly recorded as strategy nodes (e.g., rectangular OR square footprint – both extremes need to be supported by the CAD model), which in turn describe the Control building configuration action, one of the two actions required to achieve the Minimum usable area constraint. Both strategies share the same set of geometric parameters – Building width (input) and Building length (output). Using his expert knowledge on minimum usable building width, the architect suggests a range decribed by two parameter constraints – lower limit of 30 feet, and upper limit of 95 feet, calculated by using Pythagora’s theorem in view of the round site configuration.
In addressing the Maximize use of daylight goal, Introducing lightshelves – one of the five required actions, is identified as leading to a potential conflict with the Minimize construction cost goal which is important for the client. Further decomposing the action into strategies helps determines how to avoid the negative impact. For example,
84 Chapter 3: DS Methodology Victor Gane two strategies impact the geometry, the third suggests a material. Having the Same depth on all sides is a less costly solution than Orientation dependent depth strategy, which results in a higher number of custom building components, and thus is the chosen strategy. The strategy that wasn’t chosen along with the subsequent dependent nodes is faded by the system and kept as a reference in case stakeholders change their preferences in the Requirements Model.
The SM enables design stakeholders to concurrently simplify complex design decisions by visualizing each others’ logic and the repercussions, and identify key design parameters used to generate a requirements-driven logical alternative space.
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Figure 3-9: Scenarios Model for the University Quad illustrative example. The model starts with the two Constraints and two Goals transferred from the RM and design stakeholders rationalize them into Actions, Strategies, Parameters and Parametric Constraints. AND, OR, XOR logical gateways are used to describe relations between Actions, Strategies, and Parameters.
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5.5.3 Parametric Process Model
With the SM finalized, the system transfers the input and output parameters into the PPM environment. The CAD expert is notified by the project administrator to begin building the PPM used to generate the geometric alternative space. He first examines the SM to understand the design stakeholders’ logic for addressing requirements and the scenario(s) to be implemented in a CAD model. He begins building the PPM by decomposing the CAD model structure into six components created in the component- level PPM model space (Figure 3-10). In today’s practice, this step is generally fraught with errors and leads to likely rework because a comprehensive RM/SM is missing. The CAD expert organizes the nodes to reflect the sequence in the model building process and the inter-component dependency. For example, in order to generate the Windows, the model must first have the Walls component constructed, which in turn is dependent on the Building footprint component.
Figure 3-10: Component-level PPM illustrates the CAD model decomposition into six components shown in hierarchical order.
With the component-level structure of the parametric model in place, the next task is to determine the composition of each component at the schema level. For example, in describing the model’s first component called Property outline, the CAD expert referenced the RM model that helped identify the site’s circular configuration and its radius constraint. As a result, a circle was used as a starting point in building the geometry-level PPM. To help fix the circle in the work space, its origin was coincidentally constrained to the origin of the XY plane, and its radius was determined by the Property radius parameter, which includes the 10’-0” set back constraint (Property radius → 60’–10’=50’) (Figure 3-11a).
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To construct the Building footprint component the CAD expert chooses a rectangle as the geometric element given the scenarios prescribed in the SM (i.e., square to rectangle). He then assigns a geometric constraint (i.e., coincident) that binds the rectangle’s first three vertices to the circle outline, and connects with a dependency arrow the SM transferred Building width global input parameter to the vertical line on the rectangle’s right hand side. The Building length output parameter and its value is dynamically measured after the CAD expert links it to the rectangle’s horizontal line. This enables calculating the Minimum usable area constraint through the Floor area output parameter by multiplying the Building length and Building width parameters (Figure 3-11b). To prevent emergence of unpredictable geometry (e.g., changing the length of the rectangle that has not been geometrically constrained may lead to a parallelepiped), the pairs of lines are assigned vertical and horizontal geometric constraints.
a) b) Figure 3-11: Schema-level PPM describes the composition of: a) “Property outline” component; b)”Building footprint” component.
A similar method is used to construct the model’s remaining four components. Figure 3-12 illustrates the final composite PPM model, which helps understand the implications of changing the value of any input parameter on the rest of the model. CAD experts can navigate through the model by selecting component nodes and bringing into focus at the geometry-level only the nodes that are grouped into that component.
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Figure 3-12: Final composite geometry-level PPM. Note that the nodes’ attributes are toggled off to help simplify the model. The model helps understand which nodes are affected when parameter values are changed by highlighting them (i.e., the Building width value was changed from 40’ to 70’).
The completed PPM serves as a guideline to building the parametric CAD model in which design stakeholders manage the SM parameters to generate geometric design alternatives. Table 6 illustrates three such alternatives from potentially an infinite number that the CAD model can support within the SM-defined alternative space. A selection of model definition parameters is also included. Designers selected these alternatives for further analysis following both a qualitative (e.g., visual) and quantitative assessment, in which they use the Floor area output parameter to understand which alternatives satisfy the 3,000 ft2 Minimum usable area constraint.
Table 3-6: Three geometric alternatives selected for further analysis and the input and resulting output parameters.
Alternative 1 Alternative 2 Alternative 3 Floor Area (Constraint) 4,330 5,000 3,666 - 3000 ft2 Constr. Cost (Goal) - 112,809 130,655 139,250 $<100,000 Building Height (ft) 16 18 18 Building width (ft) 50 70 40 Window Height (ft) 9 10 8 Window Width (ft) 6 9 5
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Window Spacing (ft) 3 2 2 Light shelves Depth (ft) 3 4 2.5
5.5.4 Alternatives Analyses Model
Building the AAM enables the design stakeholders to make an objective decision amongst the three alternatives based on how well each alternative satisfies the value function established in the RM. First, the system transfers the RM goals and the PPM input and output parameters into the AAM model environment. Next, design stakeholders add the three alternatives to the project database, for which they record the parameter values used to generate them and the impact scores for each goal. Similar to the quantitative assessment of constraints in CAD used to select the three alternatives, the design stakeholders determine the impact scores of each selected alternative for the Minimize construction cost below $100,000 goal, calculated as the sum of Window cost, Wall cost, and Light shelves cost output parameters. For example, alternative 1 cost $112,809 or 17% above the goal target value and receives a score of 83%. Changing any of the input parameters affects the performance of this goal.
Not all goals, however, can be calculated by means of output parameters in CAD. Some require model-based analyses in specialized tools. For example, to address the Maximize use of daylight goal, the mechanical engineer optimizes the geometry of the selected alternatives (e.g., mesh) to perform daylight analyses in Autodesk Ecotect (Figure 3-13).
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a)
b)
c) Figure 3-13: Some quantifiable goals require model-based analysis performed outside the parametric modeler. Autodesk Ecotect© is used to determine average daylight values in lux for all three alternatives. Note that the ceiling is omitted for clarity.
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This enables extracting average daylight values and assigning impact scores for Maximize daylight use goal. For example, alternative 1 has an average of 400 lux, or an 80% score of the target value of 500 lux (400*100/500=80%); alternative 2 → 300 lux, or a 60% score (300*100/500=60%), and alternative 3 → 450 lux, or a 90% score (450*100/500=90%). All global input parameters listed in Table 3-6 impact this goal.
Once the stakeholders assign all the impact scores, the system generates the value scores for each alternative by multiplying the impact scores with the cumulative percentage importance scores of each goal from the RM. For example, alternative 1 value score multiplies 80*0.65=52. The system aggregates value scores for each alternative into a total value score (e.g., alternative 1 → 52+29=81) to enable the stakeholders to objectively select the highest performing alternative 1 (Figure 3-14b).
a)
b) Figure 3-14: Alternatives Analysis Model: a) users input impact scores and geometric parameter values for each analyzed alternative; b) the system generates a value score for each alternative. Alternative 1 emerges as the preferred one based on the goals identified in the RM and the goal importance determined by stakeholders.
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5.5.5 Testing the practical value of DS
We tested the practical value of Design Scenarios methodology on an industry project – the concept design of a high-rise building in Saudi Arabia by a leading Architecture Engineering firm (Figure 3-15). We present the findings in Gane et. al. (2011). When compared to the traditional conceptual design process, the results indicate an order of magnitude improvement across several metrics for measuring design space quality:
Clear objective space – the elicited constraints and prioritized goals have largely remained unchanged and served in building a scenario-specific design space;
(1) High objective space quality – the project requirements were elicited from all project stakeholders; (2) Clear option space size – unlike the traditional process, DS enabled designers to quantify the option space size; (3) Generated options space size – the CAD expert generated over 1,100 options versus an average of 3 that design team generate with the traditional conceptual design process; (4) Alternative space size – the CAD expert generated and analyzed 10 alternatives versus an average of 3 with the traditional conceptual design process; (5) Alternative space clarity – designers developed two well-defined scenarios and the relevant input and output parameters versus the design rationale ambiguity and lack of guiding parameters in the traditional process; (6) High CAD model clarity – the parametric CAD model structure was explicit and transparent; (7) High CAD model quality – the CAD expert built one CAD model per scenario used to generate the alternative space versus multiple CAD models required in the traditional process when requirements were not met; (8) High impact space size – designers performed a formal analysis for each requirement;
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(9) High value space size – designers determined a total value for each generated alternative versus a lacking valuation method in the traditional process; (10) High value space clarity – unlike the traditional process, designers explicitly defined a total value for the generated alternatives.
Design Scenarios was tested on a single industry project. However, we expect DS to have a similar impact and power on other AEC projects and industries that undergo analogous process problems.
Figure 3-15: Jeddah mixed-use towers project in Saudi Arabia.
6 Conclusion Today, the process of designing a building requires the expertise of many disciplines, in which experts tackle the same problem with different sets of requirements, ontologies, and work methods. The performance of a design project is, therefore, not only a function of the expertise of the individual experts, but also how well they work together (Garcia et. al. 2004). This is especially true during conceptual design, when decisions are the cheapest to make for design stakeholders and have biggest impact on the design cost and performance (Ellis & Torcellini, 2008). A system that is flexible enough to be considered general but which offers a common ontology and work process for all stakeholders in the building design process can positively impact the design space quality. This paper first introduced a framework for measuring design space quality through a set of key metrics for the Objective, Alternative, Impact, and Value subspaces. The paper’s major contribution is a methodology called Design Scenarios developed to enable populating the framework with metrics quantifying a conceptual design process in which the alternative space is generated with parametric
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CAD. The novel features include an ontology for building multidisciplinary logical and geometric alternative spaces, as well as the process of systematically transferring design requirements from the objective space to the alternative, impact and value spaces through the means of four interrelated models.
With the Requirements Model stakeholders explicitly determine project requirements in a unified model, in which they rank goals and understand how important these goals are to each stakeholder. With the Scenarios Model, design stakeholders rationalize the requirements transferred from the RM into logical alternative spaces described in terms of scenarios and key input and output parameters. With the Parametric Process Model CAD experts explicitly communicate the construct of parametric CAD models, in which the parameters transferred from the SM are used to generate geometric alternative spaces within the boundaries of the SM-defined scenario. Finally, with the Alternatives Analysis Model stakeholders determine the impact and total value scores for the generated alternatives to enable an objective decision making process.
This paper described the application of Design Scenarios through an illustrative example. Gane et. al. (2011) illustrate the application of Design Scenarios on an industry test case and compare the resulting metrics measuring the design space quality with three other data sets: (1) traditional, non-parametric conceptual design practice; (2, 3) two applications of parametric modeling with no formal methodology to generate and communicate scenarios. The DS methodology was tested on two scenarios for the same industry test case to illustrate its generality across requirements and scenarios. However, the current limitation is that one researcher implemented the methodology and performed the measurements from one industry test case. Future work is to extend external validity by applying DS on more industry projects. DS offers opportunities to automate the parametric CAD model generation from the PPM and integrate the AAM with multidisciplinary optimization tools to automate the process of determining the impact scores for alternatives. The industry application of the methodology presents evidence that DS provides CAD experts with well-defined logic and parameters for addressing requirements and the process enables creating
95 Chapter 3: DS Methodology Victor Gane parametric alternatives with clear multi-objective values that potentially provide clients with better building designs.
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Chapter 4: Application of Design Scenarios methodology to evaluate the effectiveness of transparent parametric CAD
Victor Gane, John Haymaker, Martin Fischer, Vladimir Bazjanac
1 Abstract Quality designs generally emerge from a conceptual design process that generates and communicates large design spaces of objectives, alternatives, impacts, and values. Parametric modeling is a popular means for generating large alternative spaces but it is difficult to use effectively when the other spaces are not well generated. We apply the framework for measuring design space clarity and quality (Gane et. al, 2011) to traditional non-parametric practice, and to two industry applications of parametric modeling on high-rise projects. The framework reveals deficiencies in both the quality and clarity of the design spaces that building designers are able to construct. We describe a third industry test case illustrating the application of a formal methodology called Design Scenarios developed to address these shortcomings. The data sets illustrate the potential for significant impact that parametric modeling can have on the overall conceptual design process performance, particularly when supported by methodologies to better generate and communicate design spaces.
2 Introduction – the need for effective conceptual design processes Last decade has witnessed increased awareness about the negative impact buildings have on the environment. In the U.S. over 70% of the electricity, 40% of raw materials, and 12% of water consumption is attributed to buildings (USGBC, 2007). The situation is not improving – the lifecycle performance of many new buildings is below that of older buildings and often below code requirements (Turner and Frankel, 2008). While design costs (5-8%) are typically dwarfed by construction costs (60- 80%) (Miller, 1993), the biggest impact and opportunities for lifecycle performance improvement are with decisions made during conceptual design, when the building’s
103 Chapter 4: Application of DS Methodology Victor Gane orientation, massing, materials, components, and systems and their properties are proposed (Ellis and Torcellini, 2008).
Quality designs generally emerge from a conceptual design process that generates and communicates large design spaces of objectives, alternatives, impacts, and values. Design requires the integration of knowledge and the simultaneous satisfaction of many functional requirements (Suh, 1995, Kraft et.al. 2007). Research shows that successful designs require an early understanding of such requirements and the ability to explore and analyze a large quantity of alternatives (Houser and Clausing, 1988, Kelley, 2006). To make the design space more manageable, designers normally adopt a scenario – a collection of structures and behaviors that represent the design intent (Baniassad and Clarke, 2004). Research identifies two primary search methods through a design space: high breadth, low depth (multiple scenarios with a broad spread of options but little analysis) and high depth, low breadth (few scenarios with a low spread of options but more comprehensive analysis) (Woodbury and Burrow, 2006, Goldschmidt, 2006). Design theory indicates that ideally, during the design process, lots of scenarios and alternatives within each scenario are generated and analyzed.
In spite of this increased awareness about the impacts of buildings, the importance of conceptual design, and areas for improving the design process, current multidisciplinary design processes have not changed dramatically. Gane and Haymaker (2010) determined that existing conceptual design processes are inefficient. We conducted a benchmarking survey to determine the performance of leading design teams. We found that during a design process that generally lasts 5 weeks a multidisciplinary team averaging 12 people can normally produce small alternative spaces, in which on average 3 design alternatives are generated. Very little of their time is dedicated to establishing / understanding the objective, impact, and value spaces. The performed analyses are inconsistent and primarily governed by architectural rather than multi-stakeholder criteria (i.e., day lighting, energy efficiency), which may often lead to major and costly redesigns when results fail to satisfy such requirements (Royce, 1970). Decisions are made and changed frequently
104 Chapter 4: Application of DS Methodology Victor Gane as specifications change and new ideas come forward, yet much of the decision making rationale is lost in the due process, or presented to the client as descriptive narratives, in which important inter-related topics are difficult to identify and comprehend (Kam, 2005). These process deficiencies lead to design solutions with often poor initial cost and lifecycle performance.
Such methods as performance-based concurrent engineering (Miller, 1993), Quality Function Deployment (Houser and Clausing, 1988), or parametric modeling (Shah and Mäntylä, 1995), which emerged in aerospace and automotive industries, can help design stakeholders formally create and explore large design spaces. However, these methods are not used broadly in conceptual Architecture Engineering Construction (AEC) design. In part this is caused by a sequential process of decision making in multidisciplinary design teams and the limited ability of CAD experts to capture the designers’ rationale, identify key design parameters, and communicate and manage the complex structure of parametric models. This makes the design process dependent on few experts and the expert knowledge hard to disseminate. To maximize the efficiency of the conceptual design process and improve the life-cycle performance of the resulting designs, our intuition is that AEC industry needs (see motivations in Gane et. al., 2011) a significantly more structured and concurrent process of constructing and communicating:
(1) Objective spaces – capture, prioritize, communicate, and manage design requirements (constraints and goals); (2) Alternative spaces – translate such requirements into geometrically flexible parametric CAD models used to generate design alternatives; (3) Impact spaces – evaluate performance of alternatives against the project requirements and (4) Value spaces – determine the value of alternatives to support improved decision making.
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Gane et. al., (2011) reviewed other research and found many points of departure, but no integrated solution that sufficiently covered all four spaces. Some methodologies, like Requirements Engineering (REF) and MACDADI (Haymaker et. al., 2011), communicate objective, impact, and value, spaces, but do not communicate alternative spaces sufficiently. Other methodologies, like parametric modeling, excel at generating alternative spaces, but fail to communicate these spaces and relate them to objectives and impacts. Gane et. al., (2011) proposed Design Scenarios to enable multi-stakeholder and multidisciplinary design teams to streamline the alternative generation and decision-making processes by providing a methodology for building and managing requirements driven design spaces with parametric tools. DS consists of four consecutively built interdependent models: (1) a Requirements Model that allows stakeholders and designers to explicitly define and prioritize context specific design requirements; (2) a Scenarios Model that helps designers formally transform these requirements into actions necessary to achieve them, and the into geometric and material parameters, interrelationships, and potential conflicts; (3) a Parametric Process Model that helps CAD experts communicate the structure of a parametric model for building requirements-driven alternative spaces and facilitate its technical implementation; and (4) an Alternative Analysis Model that helps designers to analyze and visually report performance back to the stakeholders.
To enable gauging the impact of such a process, Gane et. al. (2011) proposed a framework for measuring design space clarity and quality, which consists of the following metrics:
. Objective Space Size – what is the number of project goals and constraints considered? . Objective Space Clarity – is the value function explicitly and broadly communicated? The clarity is determined through documented statements describing stakeholders, goals, constraints, and preferences. . Objective Space Quality – are the project goals and constraints determined by all key stakeholders? A low quality denotes participation of <50% of stakeholders; medium quality: 50-80%; high quality: 80-100%.
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. Number of Scenarios – what is the number of design scenarios considered? A design scenario is a collection of structures and behaviors that represent the design intent (Baniassad & Clarke, 2004). . Total Options Space Size – what is the total number of options comprising a scenario? The total number is determined by multiplying the constrained range of values of input parameters (i.e., building length between 30m and 40m) and their reasonable increment (i.e., 1m for building length). . Generated Option Space Size – what is the number of the generated design options for a scenario? . Options Space Quality – the ratio of Total Options Space Size to Generated Options Space Size. A 1.0 ratio is ideal because it covers the complete Design Space for a given scenario. . Alternative Space Size – what is the number of the generated design alternatives for a scenario? . Alternative Space Clarity – are the design scenarios and the parameters describing these scenarios clear? . CAD Model Clarity – is the structure of the CAD model communicated? . CAD Model Quality – how many CAD models were generated for each design scenario? One CAD model is the target and denotes high quality and responsiveness. A new model is built when it cannot satisfy a requirement. . Impact Space Size – what is the number of performed analyses used to determine the value of each alternative? . Impact Space Clarity – is the process of performing each analysis explicitly depicted (i.e., repeatable)? . Impact Space Quality – what is the ratio of Impact Space Size to Objective Space Size? A 1.0 ratio is ideal (i.e., for each requirement a formal analysis was performed). . Value Space Size – what is the number of alternatives that have been analyzed and valued?
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. Value Space Clarity – is the total value of each generated alternative explicitly defined? . Process Duration – how long did the conceptual design process last? Designers favor shorter durations because it positively impacts the firm’s profitability.
In the remainder of the paper we populate this framework with four data sets, summarized at the end in table 8. Gane & Haymaker (2010) described and quantified traditional high-rise conceptual design processes in which no parametric modeling or methodology addressing the needs outlined in section 1 were used, and these data are used to populate the first column in table 8. In section 2 we describe two industry applications of parametric modeling on high-rise projects, in which no formal methodology to address the above needs was used. The framework reveals deficiencies in both the quality and clarity of the design spaces that designers are able to construct. In section 3 we describe a final industry test case illustrating the application of a formal methodology called “Design Scenarios” developed to address these shortcomings. In section 4 we compare the four data sets to illustrate the potential for significant impact that parametric modeling, supported by methodologies to better generate and communicate design spaces, can have on the design space quality and clarity. We used the action research method (Hartmann et. al., 2008) on the three test cases designed by the same leading AE firm, in which the first author of this paper had the role of the parametric CAD expert.
3 Conceptual design process using parametric modeling with no formal implementation method We now discuss two conceptual design process case studies, called Tower 1 and Tower 2, in which designers used parametric modeling in alternative generation and decision-making.
3.1 Tower 1 test case Tower 1 is a residential high-rise currently being built in the Dubai Marina. The analysis in this paper is based on the conceptual design process only. Table 4-1
108 Chapter 4: Application of DS Methodology Victor Gane summarizes a selection of project facts and requirements that guided the design process. Several qualitative and quantitative goals and constraints were proposed by the primary stakeholder (client) and design stakeholders (architect and structural engineer). The distinction among types of requirements was made retrospectively. Actual target values for the identified requirements are not shown in compliance with the client’s privacy request.
Table 4-1: Tower 1 project facts and requirements. No formal method to gather and prioritize requirements was implemented. Project Facts Project Phase Competition (Conceptual design), 2005, currently under construction Project Type Residential high-rise, 307m, 73 floors Team size and composition 1 researcher (CAD expert), 1 senior architect, 1 project architect, 1 intern, 1 structural engineer Software tools CATIA V5R14, Rhinoceros, AutoCAD, 3D Studio Max, ETABS
Requirement Description Requirement type Identified By Modern, symbolic Goal (qualitative) architecture Minimize construction cost Goal (quantitative) Gross area Constraint (quantitative) Client (developer) Building efficiency Constraint (quantitative) 20m setback from adjacent Constraint site (quantitative) Minimize heat load Goal (quantitative) Maximize views Goal (quantitative) Design stakeholders Use parametric CAD Goal (procedural)
The designers began the process by evaluating the client project brief, in which requirements without any distinction between goals and constraints were presented in narrative form and distributed among different sections of the brief. Next, designers clarified the objective space during informal meetings, in which the following goals were identified: need to address the local climate (minimizing heat load), site properties (maximizing views), and process efficiency in exploring multiple design alternatives (using parametric CAD). The choice to use parametric CAD was made in
109 Chapter 4: Application of DS Methodology Victor Gane response to the client’s objective to build a contemporary, unique building exemplifying the technological complexity of the 21st century (modern, symbolic architecture), but governed by simple rules that supported rationalization for effective construction and modularization for reducing costs. None of the requirements or the rationale used by designers to propose the requirements was explicitly captured.
Figure 4-1 illustrates the project site. The building’s irregular footprint configuration was determined by the site’s irregular shape and the 20m setback constraint from the adjacent site, as well as the gross area constraint.
Figure 4-1: Tower 1 site configuration, initial tower/podium footprint, and the required 20m setback.
The architect then proposed a design scenario of a twisting tower that helped delineate the design space boundaries. The scenario was proposed because the narrow sides of the footprint faced the best views (i.e., ocean and city as opposed to adjacent buildings). To address the maximizing views goal, the design was twisted to expose the wide sides in the tower’s top third, where the most expensive units were located, to the best views. The CAD expert identified a set of key geometric parameters and relationships in the CAD model that could enable generating the scenario-specific range of geometric variations. For example, the architect anticipated needing to refine the building twist and footprint configuration, and identified such parameters as tower rotation ranging from 0 to 90 degrees, angle controlling the kink, and the individual side lengths. The CAD expert decomposed the model into components containing geometric elements, parameters, and constraints. The model decomposition was
110 Chapter 4: Application of DS Methodology Victor Gane implemented in CAD and not explicitly visible to team members other than the CAD expert. Figure 4-2 illustrates the components and relationships of the parametric CAD model determined retrospectively. For example, the tower footprint with all the driving parameters and geometric constraints was assembled into a component and instantiated twice to enable the tower rotation (Figure 4-3a). The CAD expert only reassigned the parameter controlling the tower rotation to the middle and top footprints at the time these were instantiated. He made the top footprint rotation an input parameter and the middle footprint rotation an output parameter, with its value always being half of the top footprint rotation. The three footprints were used to create the tower envelope (Figure 4-3b).
Floor Floor Spandrel planes plates beams Tower mid footprint Tower Tower Glazing Columns footprint envelope panels Tower top footprint Column Fins profile
Figure 4-2: Components describing the high-level structure of the Tower 1 parametric CAD model.
To model the structure, the CAD expert converted a column profile controlled by global length and width input parameters into a component and instantiated it along the building footprint perimeter (Figure 4-3 c). Next, he added two additional input and output parameters – the number of columns and spacing in between them, followed by floor planes controlled by the number of floors and floor height input parameters. He then extruded the column profiles to create 3D columns that depended on the tower envelope (used as a construction element) and the floor planes as extrusion limits (Figure 4-3d). A similar process was used to create the rest of the model’s components (i.e., glazing panels, fins, floor plates, spandrel beams). The final model described a detailed, generic floor with the geometric flexibility required to
111 Chapter 4: Application of DS Methodology Victor Gane generate and assemble the three floor types with unique heights – typical residential, penthouse, and mechanical (Figure 4-3e).
a) b) c)
d) e)
Figure 4-3: Tower 1 parametric model: a) the tower footprint (plan view) instantiated twice with input parameters controlling the tower rotation; b) tower envelope (perspective view) lofted from the three footprints; c) small section of the footprint with a column component and the driving parameters (perspective view); d) columns extruded along the footprint (perspective view); e) final model of a single floor used to create ~1,000 design options (perspective view).
By updating the values of building side length and floor height parameters, the design stakeholders were able to investigate which alternatives satisfied the gross area and tower efficiency constraints, both established as output parameters in the CAD model. To address the minimizing the construction cost goal, designers altered the input parameter values determining the spandrel beam depth and column length & width. Structural Engineers formally analyzed in ETABS the resulting design alternatives to determine structural viability and cost. As a result, the structural design evolved from the initial columns with double curvature to columns with single curvature, which
112 Chapter 4: Application of DS Methodology Victor Gane dramatically reduced the cost but maintained the original architectural design intent (Figure 4-4). The final solution required amending the CAD model by adding a stepped extension to allow the rebar connections between columns. Output parameters calculating the surface area of components such as glass and cladding, and the volume of components such as columns and spandrel beams, were used to dynamically calculate rudimentary cost estimates.
a) b) c) Figure 4-4: The team investigated three structural alternatives using the same parametric CAD model. a) originally proposed solution with expensive double curvature; b) intermediate solution with single curvature but with architecturally unappealing columns; c) final solution with single curvature stepping columns, in which fins cover the step from top to bottom column. Column and fin sizes were varied to minimize the heat load. The glazing offset from exterior wall was adjusted to satisfy the gross area & efficiency constraints.
To address the minimize heat load goal designers iterated the values of parameters determining the window setback in relation to the building’s exterior face, as well as the column size, which controlled the fin size. The objective was to minimize the glazed surface area impacted by direct sunlight. The actions taken to address this goal were carefully coordinated with the gross area and efficiency constraints.
The CAD model was operated through 13 input parameters illustrated in Table 4-2 and used to generate 15 alternatives (Figure 4-5). The model enabled the research team to determine the Total Option Space Size metric for the scenario-specific design space by multiplying the total number of steps for the input parameters by the input parameters’ increment (see Table 4-8).
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Table 4-2: Input parameters and constrained ranges. Input parameter Constrained range Parameter Total steps name increment Core diameter 13-15m 1m 3 Circulation corridor 17-18m 1m 2 diameter Tower width 30-40m 1m 11 Tower length 20-30m 1m 11 Tower rotation 45-90deg 1deg 46 Building side angle 150-170deg 1deg 21 Tower height 300-330m 1m 31 Column spacing 3.0-3.6m 0.5m 14 Column length 0.5-1.0m 0.1m 6 Column width 0.5-0.9m 0.1m 5 Floor height 3.6-4.5m 1m 9 Slab depth 0.2-0.3m 0.1m 2 Spandrel beam depth 0.5-0.9m 0.1m 5
a) b) c)
Figure 4-5: Multiple Tower 1 alternative twist values were parametrically investigated during the design process. a) 60 degree twist; b) 90 degree twist; c) final design featuring a 90 degree twist and smaller glazing setback.
3.2 Tower 2 test case Tower 2 was a high-rise competition in San Francisco. Table 4-3 summarizes a selection of project facts and requirements that guided the design process. Two types of requirements were considered: qualitative constrains proposed by the primary stakeholder (client), and one procedural goal by the design stakeholders (architect and structural engineer).
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Table 4-3: Selection of Tower 2 project facts and design requirements Project Facts Project Phase Competition (Conceptual design), 2007 Project Type Multiuse high-rise (office, hotel, residential), 1375ft 1 researcher (CAD expert), 2 senior architects, 3 mid-level Team size and composition architects, 2 senior structural engineers, 1 structural engineer, 2 mechanical engineers DigitalProjectV1R2, Rhinoceros, AutoCAD, 3D Studio Max, Software tools ETABS, Ecotect, Flovent, Virtual Wind
Requirement Description Requirement type Identified By Office gross area Constraint (quantitative) Office 1st floor gross area Constraint (quantitative) Client (developer) Office last floor gross area Constraint (quantitative) Hotel gross area Constraint (quantitative) Use parametric CAD Goal (procedural) Design Stakeholders
The design process started with a poorly defined objective space containing only a limited set of constraints proposed by the client. The design stakeholders did not explicitly delineate any design scenarios they were interested in exploring but rather pursued the conventional practice of gradual, informal clarification of the design intent.
Designers generated a total of 20 alternatives from six different parametric models. Figure 4-6 shows a selection of seven alternatives generated with four of the six models. For each model, the CAD expert required one week to understand the senior architect’s emerging scenario and translate it into a CAD model through a technical process similar to the one described in Tower 1 case. The major distinction, however, was in how design stakeholders and CAD experts interacted. The senior architect occasionally reviewed the in-progress CAD model generated by the CAD expert, who was not clear of the design scenario, and made improvised suggestions. For example, the CAD expert built the first model based on a design precedent that the senior architect developed for another project and adapted it to address the client constraints (Figure 4-6a). However, it was soon determined that the resulting aspect ratio of 1:10
115 Chapter 4: Application of DS Methodology Victor Gane and the lease span on multiple floors were unacceptable. A relatively quick check could have helped invalidate this segment of the design space had these requirements been explicitly defined early on. As a result, the senior architect decided to investigate a geometrically and structurally different scenario (Figure 4-6b). This invalidated the original CAD model, which given its geometric and relational complexity took significant time to build. A similar process was repeated on all consecutive models. This lack of procedural rigor dramatically reduced the effectiveness of parametric CAD tools in a conceptual design process that lasted longer than average (see Table 4-8).
a) b) c) d)
Figure 4-6 (a – d): 7 alternatives from over 1,000 generated options. The lack of a formal methodology for defining and translating design requirements into parametric models led to the construction of six unique models to generate 20 alternatives.
The CAD expert operated the CAD model in Figure 4-6a through 9 input parameters and ranges illustrated in Table 4-4: Input parameters and constrained ranges describing the model in Figure 4-6a, which enabled the research team to determine the Total Option Space Size metric. Table 4-8 summarizes the third data set describing the resulting conceptual design process performance.
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Table 4-4: Input parameters and constrained ranges describing the model in Figure 4-6a. Input parameter name Constrained Parameter Total steps range increment Bot. triangle base length 170-190ft 1ft 21 Bot. triangle side length 160-170ft 1ft 11 Top triangle side length 90-100ft 1ft 11 Bottom triangle chamfer 20-30ft 1ft 11 Chamfer angle 60-90deg 1deg 31 Bottom centroid offset – origin 50-65ft 1ft 16 Top centroid offset – origin 50-60ft 1ft 11 Office floor height 13-15ft 1ft 3 Residential floor height 10-12ft 1ft 3
3.3 Summary In summary, both test cases illustrated an effectively new conceptual design process in which with parametric CAD design teams build systems for developing large design spaces rather than point solutions. However, neither case represents a methodology that would enable designers to optimize or repeat the process. The designers had no formal method to capture, manage, and rationalize design requirements into effective parametric CAD models. They were unable to make the structure and rationale of those models clear to the entire team, which renders the process they developed hard to integrate with analysis, and to repeat even within the same firm. For example, as Tower 1 progressed into the next phase a few months after the concept design submission, even the CAD expert who built the CAD model required a substantial time investment to restore his understanding of the model structure and the means to operate it. The lack of such a method in larger teams leads to significantly poorer results as was illustrated in the Tower 2 test case. In both cases design stakeholders finished the conceptual design process without a clear understanding of the potential value of the alternative space.
Overall, Tower 1 proved more successful. In spite of a demanding schedule, the design stakeholders effectively addressed the project requirements and delivered a geometrically complex and architecturally engaging design that mostly addressed economic requirements (Figure 4-5). In three weeks, a single CAD model was built for
117 Chapter 4: Application of DS Methodology Victor Gane one scenario and used to investigate nearly 1000 design options refined into 15 alternatives. Designers made this possible by implicitly defining the objective space, translating requirements into key parameters, and following a scenario that remained largely unchanged throughout the design process. The success of the project was due in part to the small team size with few design stakeholders (architect and structural engineer only, making it an objective space of medium quality), the expertise in using parametric CAD (one architect built and operated the model), and its diligence in observing the project requirements with which it started the design process.
Next we discuss the application of Design Scenarios (DS) methodology on a third high-rise test case. DS was implemented into a web-based software prototype to help enhance the application of parametric CAD in conceptual design and enable design stakeholders to generate and communicate clearer and better design spaces. DS consists of four consecutively built interdependent models. With the Requirements Model (RM) project stakeholders explicitly define the context specific objective space and prioritize goals. With the Scenarios Model (SM) design stakeholders build the logical alternative space by formally transforming the objective space into geometric and material parameters, establishing parameter interrelationships and identifying potential conflicts. With the Parametric Process Model (PPM) CAD experts build the geometric alternative space by illustrating the technical implementation of a SM in a parametric model used to generate design alternatives. With the Alternative Analysis Model (AAM) design stakeholders analyze alternatives to determine the impact and value spaces.
4 Conceptual design process using Design Scenarios (DS) to clarify design spaces We tested the impact of Design Scenarios methodology on an industry supported test case called Tower 3 – a mixed use project in Jeddah, Saudi Arabia consisting of two towers – an all residential (Tower 1) and a mixed use (Tower 2 - hotel and serviced apartments). The project team developed four scenarios using traditional concept design methods similar to those described in Gane and Haymaker (2010). The research
118 Chapter 4: Application of DS Methodology Victor Gane team concurrently built and shared DS models with the project team for two of these scenarios. Several project facts are summarized in Table 4-5. Next, we describe the DS process and the resulting models and present the four data sets.
Table 4-5: Project facts. Project Facts Project Phase Conceptual design Project Type 2 high-rises – hotel and mixed-use Team size and composition 1 researcher (CAD expert), Client (developer), Design Architect, Technical Architect, Mechanical Engineer, and Structural Engineer Software tools Digital Project V1R4, Rhinoceros, 3D Studio Max, Ecotect, Radiance
4.1 Summary of Tower 3 design team process While the research team was building the Requirements Model, it became apparent that the design team had no common understanding of what were the project requirements, how to address them, or what was the reasoning used to formulate these requirements. For example, one such requirement was the 145m height to last inhabited floor constraint defined by the client. The site’s proximity to the airport required the client to pay the city for any additional floor above 145m, which would negatively impact the profit margins. Most of the design team was not aware of this fact. Furthermore, designers did not distinguish between goals and constraints or the difference in their importance level. The team often had to wait for instructions from the design architect on how to proceed, making the process highly inefficient. The four design scenarios were generated ad-hoc based on precedents of high-rise typologies and not the project’s guiding requirements. Architects were the only contributors in the design process. As a result, during the first progress meeting with the client two weeks into the project, the team was unable to successfully convey the reasoning used to address the client’s primary goal of maximizing views and the difference in the performance of the presented alternatives. This invalidated most of the design team’s work, which had to start over. The team used traditional, non-parametric CAD to generate one option per design scenario, which confirmed our benchmarking study of current conceptual design process performance (see Table 4-8).
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4.2 Requirements Model (RM)
The DS process started with project stakeholders clarifying the Objective Space by building the RM. All five project stakeholders were asked to determine and record relevant project constraints and goals. First, designers analyzed the contextual constraints (i.e., site, geographic location, climate) and determined the design scenarios to be explored.
The test case site is located between the city center and the airport and is irregularly shaped as a “half teardrop”. The design architect proposed four design scenarios to be explored – half teardrop to mimic the site configuration, triangular, oval, and tapered. The research team developed two scenarios in DS (half teardrop and triangular). Figure 4-7 illustrates the site for the first scenario.
Figure 4-7: Test case “half teardrop” site and the “half teardrop” scenario tower footprint.
The research team started building the RM by first examining the requirements used by the project team to commence the design process. The only available formal resource was a booklet specifying the following three constraints: (1) Gross area for Tower 1 (55,000 sq m - residential), (2, 3) Gross area for Tower 2 (40,000 sq m – hotel, 50,000 sq m – serviced apartments). We engaged various members of the architectural team and identified six additional requirements not formally captured before - four constraints: (1) Maximum tower height to last inhabited floor – 145m, (2) Maximum site coverage – 60%, (3) North site setback – 12m, (4) South site setback - 3m, and two goals: (1) Maximize exposure to water of 100% units, (2) Sleek design. Unlike the project team, we also engaged the lead Mechanical Engineer to identify
120 Chapter 4: Application of DS Methodology Victor Gane three additional goals determined by the climate conditions in Saudi Arabia: (1) Minimize direct sunlight in 100 % units (to address undesired brightness), (2) Minimize solar heat load in 100% units (to help minimize the building cooling costs), (3) Maximize exposure to prevailing wind of 100% units (to help naturally cool the building exterior envelope). Engaging the Technical Architect and Structural Engineer did not reveal any additional requirements that these decision makers were interested in pursuing. The Mechanical Engineer, however, was very excited about his contribution and commented that “Every project should start this way”.
Using the RM tabular interface, the research team recorded the discovered constraints and goals (both quantitative and qualitative) along with the responsible stakeholder and discipline (Figure 4-8 a-b). All five decision makers were then interviewed to determine their preferences with respect to the identified goals (Figure 4-8 c).
a)
b)
c) Figure 4-8: Requirements Model inputs. Project stakeholders: (a) determined 7 quantitative constraints and (b) 5 quantitative and qualitative goals; (c) each stakeholder indicated his/her preference for the 5 identified goals by distributing 100 percentage points. Note that this example is showing only the Design Architect preferences.
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Figure 4-9 illustrates the system generated goal importance graph, in which stakeholder preferences were normalized to 100 points. The graph enabled the research team to understand individual preferences, as well as the overall relative importance of each goal. Maximizing unit exposure to water emerged as the leading goal with 46% out of 100 of overall preferences, while maximizing exposure to prevailing wind became the least important goal with only 7%.
Figure 4-9: Requirements Model outputs - the system generates the goal importance graph and normalized decision makers’ preferences to 100 points.
Building the RM enabled the research team to determine the Objective Space size of 12 requirements (7 constraints and 5 goals). The process of aggregating stakeholder requirements, assigning preferences, and building the RM lasted 1 day.
4.3 Scenarios Model (SM) The SM is a process model built by design stakeholders to explicitly determine the logical alternative space, in which requirements are decomposed into enabling parameters and relationships. Constraints and goals determined in the RM are mapped by the system into the SM, where design stakeholders concurrently decompose requirements into action items (actionable descriptions of how to achieve requirements), strategies (decision making process required to achieve an action item), parameters (variables denoting either geometric or material properties that impact a design requirement), parameter constraints (fixed value or range of values shown as lower and upper limit nodes that a parameter might be required to be within), and first order logic gateways (describe relationships between actions, strategies, and parameters. AND – all on, OR – at least one on, XOR – at least one on and one off). The SM ontology was implemented in the software prototype as visual representations that build on Unified Modeling Language object diagram formalism. The research
122 Chapter 4: Application of DS Methodology Victor Gane team engaged design stakeholders to explicitly capture the rationale each design discipline used in addressing individual constraints and goals and determine how these logically interrelate. The following section depicts this process for one constraint only. A similar process was used to rationalize the remaining constraints and goals, which we illustrate in the Appendix section of this paper.
Constraint No. 1 – Tower 1 Gross Area
To enable CAD experts to address this constraint in a parametric CAD model, the research team (acting as the Design Architect) proposed three action items: “Control the half teardrop configuration”, “Calculate gross area”, and “Control number of floors” (Figure 4-10). An “AND” relationship indicates that all three items were required to be implemented. The research team further clarified the first action item by proposing three strategies for how to control the building configuration – “Straight base only”, “Curved sides only”, and “Individually all sides”. Interested in attaining geometric flexibility, the design architect chose only the third strategy, illustrated through a “XOR” relationship. The system faded the two strategy nodes that were not chosen.
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Figure 4-10: “Half teardrop” Scenarios Model for constraint No. 1 – design architect decomposed the constraint into Action Items, Strategies, Parameters, and Parameter Constraints and determined how these relate to each other. Faded nodes indicate strategies considered, but not chosen, to be implemented in the parametric model.
The design architect now had enough information to determine the parameters that address each action item or Strategy. Given the chosen “half teardrop” design scenario, researchers identified three key parameters to enable controlling all sides individually – “Base length”, “Major arc radius”, and “Minor arc radius”. However, one of the arcs could not be user controlled because of the required geometric continuity represented by a tangency relationship between the two arcs. The design architect decided the “Base length” and “Major arc radius” to be input parameters and the “Minor arc radius” an output parameter. Next, the architect experimentally through sketches determined constrained ranges beyond which the input parameters would result in invalid solutions. For example, any value below 45m for minor arc radius resulted in a footprint that violated the site set back constraints.
Similarly, the design architect identified two parameters that enable “Calculating the gross area” action item. The “Single floor gross area” output parameter was calculated by measuring its value in CAD when either of the two footprint input
124 Chapter 4: Application of DS Methodology Victor Gane parameters were modified. The “Gross area” became a user defined input parameter that enabled calculating the “Number of floors” output parameter, which addressed the third and last action item.
SM outputs
Clarifying rationale enabled analysis of that rationale. Figure 4-11 illustrates the impact of actions on requirements. “Controlling half teardrop configuration”, for example, is the action with the impact on the most number of requirements. As a result, when searching through the design space, designers focused on but were not limited to the following input parameters addressing high impact action items: “Tower 1 base length”, “Tower 1 major arc radius”, “Tower rotation”, and “Unit width”.
Figure 4-11: SM output – Actions impact on requirements graph. ‘Control half teardrop configuration”, “ Control tower orientation and “Control unit width” emerged as Action Items that impacted most project requirements.
Building the SM enabled determining the Total Option Space Size metric for the “half teardrop” scenario. Table 4-6 illustrates the 13 input parameters used to operate the parametric model (see the complete SM model in the Appendix section for parameter sources).
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Table 4-6: Input parameters and constrained ranges. Input parameter Constrained range Parameter Total steps name increment base length 80-90m 1m 11 major arc radius 45-60m 1m 16 Unit width 4.2-4.6m 0.1m 5 floor height 3.0-3.6m 0.1m 7 South units view 30-50deg 1deg 21 vector North units view 50-60deg 1deg 11 vector City units view vector 50-60deg 1deg 11 Tower rotation 0-10deg 1deg 11 Frit density 20-50% 10% 4 South wall inclination 1-5deg 1deg 5 East wall inclination 1-5deg 1deg 5 West wall inclination 1-5deg 1deg 5 North wall inclination 1-5deg 1deg 5
The process of aggregating individual stakeholder inputs on how to address requirements and building the SM for both scenarios lasted 1 man-day.
4.4 Parametric Process Model (PPM) The PPM is a process model built by CAD experts to explicitly determine the geometric alternative space, in which the structure of dependencies between parameters, geometric constraints, CAD operations, and geometry is established. Parameters identified in the SM are mapped by the system into the PPM and used to control the CAD model’s geometry. The PPM enables making the CAD model structure clear and disseminating expert knowledge needed to enhance the application of parametric CAD in conceptual design. The PPM consists of two levels of information abstraction implemented as process model nodes in the software prototype – (1) components, which are information containers describing the component-level decomposition of the CAD model, and (2) detail-level description of the components’ composition, in which input and output parameters determined in the SM are first linked to geometric elements (predetermined geometric primitives used to create the geometric representation of the intended design), then relationships among geometric elements are established through geometric constraints (e.g., tangency, parallelism),
126 Chapter 4: Application of DS Methodology Victor Gane and CAD operations (e.g., extrude, join) are used to modify geometric elements in the direction specified by reference elements (e.g., XY plane).
The CAD expert used the “half teardrop” scenario determined in the SM to first organize the parametric CAD model structure into 18 components (Figure 4-12). The graph communicates the hierarchical dependency of components (i.e., any change to the “Floor Plate” will affect the “Slabs” component) and the CAD model construction sequence (i.e., “Slabs” can be built only after the “Floor Plate” was built). Unlike the process of building traditional, static CAD models, such distinction is critical when building parametric models.
Figure 4-12: Component-level PPM showing the parametric model structured into 18 components.
Figure 4-13 a & b illustrate the detail-level description of the “Floor Plate” component only and its graphical preview. The CAD expert used the “XY Plane” as the reference needed to determine the orientation in space of the “Ground Floor Sketch”, composed of “Line.01”, which defines the tower straight base, “Major Arc”, and “Minor Arc” defining the curved sides of the tower. He used three input and one output parameters mapped by the system from the SM to geometrically control the footprint. For example, “Tower 1 base length” controlled the length of “Line.01”, etc. “Line.02” was a horizontally constrained construction element used only as a reference when rotating the tower footprint. The “Tower rotation” input parameter defined the angle between “Line.01” and “Line.02”. The CAD expert tangentially constrained the “Major Arc” and “Minor Arc” at the overlapping vertices. He
127 Chapter 4: Application of DS Methodology Victor Gane extracted the value of “Tower 1 minor arc radius” output parameter by measuring the radius of the “Minor Arc”, which updated each time the “Major Arc” radius value was changed. He coincidentally inter-constrained the vertices of “Line.01”, “Major Arc” and “Minor Arc” to enable applying parametric adjustments globally (without these constraints, changing the “Tower rotation” value will reposition only “Line.01”).
a) b)
Figure 4-13: a) Detail-level PPM for the “half teardrop” scenario showing the composition of the “Floor Plate” component; b) Test case tower floor plate preview.
The CAD expert used a similar method to construct the remaining 16 components not shown in this paper. Note that component 1 represented the imported 2D geometry of the project site. The complete PPM can be accessed online by contacting the authors. The process of building the PPM for both scenarios lasted 2 man days.
Parametric CAD model
The PPM specifies the structure of the parametric CAD model built by the CAD expert and used to explore the design space. Table 4-7 illustrates a selection of 10 alternatives that satisfied the project constraints from ~1100 generated options, and the key input parameter values used to generate these. A total of 13 input parameters were
128 Chapter 4: Application of DS Methodology Victor Gane modified during the option generation process (4 main parameters only are shown below).
Table 4-7: 10 design alternatives and a selection of input parameters used to generate each alternative.
The CAD model building process for both scenarios lasted 3 man days, while the generation of design options and alternatives lasted 2 days.
4.5 Alternatives Analysis Model (AAM) The AAM is a tabular model that provides design stakeholders with the framework to determine and understand the scenario’s Impact and Value Spaces. It is a tool to compare the quantitative and qualitative analyses of design alternatives and determine their relative value to enable an objective decision making process. Building the AAM requires design stakeholders to evaluate how each design alternative ranks in relation to the goals identified in the Requirements Model. A simple scoring system was designed for this purpose. A design alternative receives 100% score for a given requirement if it meets its target value, which serves as a benchmark for determining the score when the target value is not met or is exceeded.
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Using the DS framework, the research team first assessed the geometry-based requirements by means of output parameters. In other words, the SM enabled building a CAD model that served in assessing all seven constraints and three goals. Each time a design option was generated, the research team assessed real-time whether constraints were met and discarded the non-conforming options. For example, all generated options satisfied the “Gross area” constraint for both towers because “Tower gross area” input parameter value was kept constant. However, changing the value of “Tower base length” or “Tower major arc radius” high impact parameters determined the values of “Tower single floor area”, “Tower number of floors”, and “Maximum tower height to last inhabited floor” output parameters, which in turn impacted five constraints and five goals.
To determine how well each of the ten design alternatives satisfied the two goals related to energy and daylight required conducting model-based analysis in specialty tools. The process wasn’t automated and the research team extracted the geometry for all ten alternatives in a format optimized for the required analysis tools (i.e., meshed exterior only with no material properties assigned - for Incident Solar Radiation (ISR); meshed with material properties of both exterior and interior - for daylight). Autodesk Ecotect™ was used to calculate the ISR. Figure 4-14 illustrates three of the ten analyzed alternatives. Three floors only were analyzed because the site was not surrounded by any tall buildings that might have impacted the analysis results.
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a) b)
c)
Figure 4-14: ISR analysis false color map (blue indicates less radiation, red – more); a) Alternative 1 – Worst (197,090 Wh/m2); b) Alternative 7 – Best (107,143 Wh/m2); c) Alternative 10 – surprising outcome (168,436 Wh/m2).
The goal of ISR analysis was to determine which alternative accumulated the smallest amount of direct solar radiation annually from 8am to 6pm – one of the most important goals of the project. Alternative 7 emerged as the best, given its floor plate configuration, orientation, floor height (h), slab offset from exterior glazing (d), and the balcony exterior face inclination (α) (Figure 4-15). The cumulative value in Wh/m2 was calculated from individual data points of the analysis mesh. Alternative 10 was expected to be the worst performer given the vertical balcony exterior face and the slab flushed with exterior glazing. However, a 0.1m reduction in the floor height was significant enough to make Alternative 10 perform better than Alternative 1.
Figure 4-15: Key parameters affecting the amount of direct solar radiation accumulated by the tower’s exterior.
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Radiance (DOE, 2011) was used to calculate the daylight values and determine the alternative with least direct sunlight in units. Figure 4-16 illustrates simplified floor plates with partitions denoting hotel units for three of the ten analyzed alternatives. Alternative 8 emerged as the best and Alternative 1 the worst, because Alternative 1 has 60cm smaller floor height, 20cm deeper slab offset from exterior glazing, 15deg larger city center view vector angle, 30deg larger south view vector angle, and 3deg smaller tower rotation angle.
(a) Alternative 1 (b) Alternative 8
(c) Alternative 10
Figure 4-16: Daylight analysis – false color map (blue indicates less daylight, yellow – more); a) Alternative 1 – Worst (4,022,200 lux / floor); b) Alternative 8 – Best
(2,205,150 lux / floor); Alternative 10 – surprising outcome (3,411,600 lux / floor).
Figure 4-17(a, b) summarize the performance scores for all ten alternatives on all of the requirements. First, the design stakeholders added 10 alternatives to the tabular model and assigned impact scores to the five project goals mapped by the system from the RM. Then, designers used best performing alternatives as benchmarks for goals 2, 3, and 5, which enabled scoring the remaining alternatives’ performance. Goal 5 was the only qualitative requirement. It was assessed by comparing all ten alternatives and selecting the preferred one (Alternative 5 – 100% impact score) used as a benchmark
132 Chapter 4: Application of DS Methodology Victor Gane against which others were compared. The evaluation was based on such criteria as overall proportions (i.e., building length vs. height, crown height vs. inhabited section height), and tactile quality of the exterior envelope (i.e., slab depth and inclination of balcony exterior face) – a subjective assessment by a human designer. a)
b)
Figure 4-17: AAM “half teardrop” design scenario; a) impact scores for 10 design alternatives and 5 goals; b) system generated value scores – overall, Alternative 7 emerged as the most successful.
After all the impact scores were assigned, the system generated the Alternatives’ Value Scores, calculated by multiplying the alternative’s impact score for every goal by the importance percentage of each goal determined by the project stakeholders in the RM and summing these into a total value score. For example, Alternative 1 received a 68% impact score for “Maximizing unit exposure to water” goal. Its relative value score was 31.3 (68*46%=31.3).
A similar process was used to generate ten design alternatives for the “triangular” design scenario and determine the total value sores of each alternative. Figure 4-18 illustrates one of the ten alternatives, and Figure 4-19 summarizes the value scores.
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Figure 4-18: Alternative 1 of “Triangular” design scenario.
Figure 4-19: AAM “Triangular” design scenario; System generated value scores – overall, Alternative 8 emerged as most successful.
5 Conclusions and future opportunities This paper presented three industry test cases, in which parametric modeling was used to search through large design spaces. Tower 1 and 2 cases employed parametric modeling without any formal method of eliciting requirements, translating requirements into input and output parameters, and determining the value of the generated design alternatives. Tower 3 case illustrated the application of a formal method called Design Scenarios. Table 4-8 compares the three new data sets with the earlier benchmarked current practice.
Design theory has extensively looked into the design space exploration topic. Woodbury and Burrow (2006) distinguish three main areas of research, all of which were in part or fully addressed in the presented test cases: (1) the premise that
134 Chapter 4: Application of DS Methodology Victor Gane exploration is a good model for designer action; (2) strategies and tools that amplify designer action in exploration; (3) development of computational structures to support exploration and represent the design space. Conceptual design offers most opportunities for design space exploration (Barrie and Paulson, 1991). Akin (2001) defines conceptual design in terms of several process steps: (a) identify requirements; (b) prioritize requirements; (c) develop preliminary solutions; (d) evaluate solutions. Test cases 1 and 2 illustrated that process steps (a), (b), and (d) are not consistently implemented and clearly communicated in current practice. The data sets, however, indicate a substantial improvement in the Option Space and Alternative Space size over current practice. The Impact Space Quality metric for both cases is lower than in current practice. It is essential, however, to view this metric in conjunction with Objective Space Quality, which in current practice is lowest.
Table 4-8: A comparison of four data sets quantifying conceptual design process performance. Items in bold denote significant improvements over current practice. Current Metric Tower 1 case Tower 2 case Tower 3 case practice Objective Space Size 3 8 5 12 Objective Space Clarity no no no yes Objective Space low medium medium high Quality Number of Scenarios 3 1 6 2 Total Option Space unknown 821.8 bil 1.37 bil 430.44 bil Size Generated Option 3 ~900 ~1200 ~1100 Space Size Options Space Quality unknown 913.11 mil 1.14 mil 391.31 mil Alternative Space Size 3 15 20 10 partial no (parameters Alternative Space (scenario only, no determined yes Clarity parameters retrospectively) retrospectively) CAD Model Clarity no no no yes CAD Model Quality 3 1 6 1 3 4 4 (area, (area, Impact Space Size (area, FEA, 12 aesthetics, efficiency, CFD, ISR) efficiency) FEA, cost) Impact Space Clarity no no no partial Impact Space Quality 1 0.5 0.8 1 Value Space Size 0 (no formal 0 (no formal 0 (no formal 10
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valuation valuation valuation Value Space Clarity no no no yes Process Duration 5 weeks 3 weeks 6 weeks 3 weeks
The fourth data set, describing the process in which design space was clarified using the DS methodology, shows improvement in several additional metrics. The SM enabled: (1) design stakeholders to make the Objective and Alternative Spaces clear by explicitly capturing the value function for each design scenario described through input and output parameters, formulas used to define parameters, and the range of acceptable parameter values; (2) CAD experts to construct parametric models used to search the requirements specific segment of the design space; (3) design stakeholders to identify high impact action items by means of upstream and downstream dependency propagation. The PPM enabled making the parametric CAD model structure clear. This clarity should help disseminate expert knowledge, which has been an impediment in the wide adoption of this modeling technique in practice. By utilizing the SM-determined parameters, the PPM can also help improve the CAD model quality metric by eliminating the need to construct multiple models for a given design scenario. However, building and communicating scenarios explicitly may impact the number of scenarios that designers are able to construct. More research is needed to determine this impact.
The DS AAM enabled the design stakeholders to make the Impact and Value Spaces clear by analyzing the performance of all generated alternatives against all project goals. The research team used the outputs for both design scenarios to perform an objective comparison and determine which scenario overall performed better for the same set of constraints and goals, as well as identify the winning design alternative. In 9 out of 10 cases the “half teardrop” scenario performed better and its winning Alternative 7 had a substantial value score difference in comparison with the winning Alternative 8 for the “triangular” scenario. AAM enables design teams to make objective decisions when faced with lots of choices, something that was impossible in test cases 1 and 2.
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Design Scenarios received some accolades from the AE firm’s design stakeholders. The Sustainable Design Group leader highlighted that “DS is a tool to quantitatively compare the results of different teams. It helps guide the team, document decisions and the reasons.” A Technical Architect and Studio Head pointed out that “DS has given us the ability to provide the client with a better product”. The Computational Design Leader stated that “DS encourages participation from people that otherwise get involved later in the design process.”
The merit of this paper was to provide evidence that increasing the design space clarity and rationale used to construct these spaces leads to improved application of parametric modeling and enables an objective design decision making process. We provided a test bed to systematically investigate the question of how much rationale needs to be made explicit in different contexts (Moran and Carroll,1996). However, we acknowledge several important opportunities to further the research presented in this paper. Table 8 illustrates that we did not have enough data to complete the proposed metrics set. For current practice, no distinction was made between scenarios and alternatives when we collected the data. That is, we collected the number of scenarios, and number of alternatives, but not the number of alternatives within each scenario. Furthermore, our survey asked to retrospectively quantify traditional conceptual design process performance. However, current design methods do not enable practitioners to quantify the number of input parameters and constrained ranges to help determine the Total Option Space Size and Options Space Quality metrics. More test cases are required to determine the comprehensiveness of the proposed set of metrics as well as better understand which method leads to more lateral thinking – Design Scenarios or parametric modeling with no formal method of clarifying design spaces.
The impact of Design Scenarios can be further expanded by addressing the following opportunities: (1) use the PPM to fully automate the parametric CAD model generation from PPM nodes by leveraging the parametric modeler’s Application Programming Interface; (2) use Process Integration and Design Optimization methods (Welle & Haymaker, 2011, Flager et. al., 2008) to automate the process of performing
137 Chapter 4: Application of DS Methodology Victor Gane multidisciplinary analyses and determining the impact scores in the AAM. The anticipated impact is a substantial increase in the Option and Alternative Space size, Options Space Quality, Value Space Quality, and a further reduction in the overall conceptual design process duration.
6 Bibliography Akın, Ö. (2001). “Variants of design cognition”. Design Knowing and Learning: Cognition in Design Education. Eastman, C., Newstetter, W., & McCracken, M., Eds., New York: Elsevier, pp. 105–124.
Alexander, C., Ishikawa, S., Silverstein, M. (1977). “A pattern language : towns, buildings, construction”. Center for Environmental Structure, vol. 2, Oxford University Press, New York.
Baniassad, E., Clarke, S. (2004). “Theme: An Approach for Aspect-Oriented Analysis and Design”. Proceedings of the 26th International Conference on Software Engineering (ICSE’04), Vol. 5, No. 3b, pp 69 – 92.
Barrie, D., Paulson, B. (1991). “Professional Construction Management: Including CM, Design-Construct and General Contracting.” McGraw-Hill, Inc.; 3rd edition.
Buschmann, F., Meunier, R., Rohnert, H., Sommerlad, P., Stal, M. (1996). “Pattern- Oriented Software Architecture: A System of Patterns”. John Wiley & Sons.
Clevenger, C., Haymaker, J. (2010). "Design Exploration Assessment Methodology: Testing the Guidance of Design Processes". CIFE Technical Report 192.
Chachere, J., Haymaker, J. (2008). "Framework for Measuring Rationale Clarity of AEC Design Decisions". CIFE Technical Report 177.
DOE, Radiance Homepage, US Department of Energy, Washington, D.C., Accessed in March 2011, from: http://radsite.lbl.gov/.
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Flager F., Welle B., Bansal P., Soremekun G., Haymaker J. (2009). “Multidisciplinary Process Integration and Design Optimization of a Classroom Building.” Journal of Information Technology in Construction (ITcon), Vol. 14, pg. 595- 612.
Garlan, D., Allen, R., Ockerbloom, J. (1994). “Exploiting style in architectural design environments”. Proceedings of SIGSOFT’94: The Second ACM SIGSOFT Symposium on the Foundations of Software Engineering, ACM Press, pp. 175–188.
Gane, V., Haymaker, J. (2010). “Benchmarking current high-rise conceptual design processes”. ASCE Journal of Architectural Engineering. Vol. 16, No. 3, pp. 100-111.
Gane, V., Haymaker, J. (2011). “Design Scenarios – enabling requirements-driven design spaces”, CIFE Technical Report No. 194. (http://cife.stanford.edu/online.publications/TR194.pdf)
Giesecke, S., Hasselbring , W., Riebisch, M. (2007). “Classifying architectural constraints as a basis for software quality assessment”. Advanced Engineering Informatics, Vol. 21, pp. 169–179.
Goldschmidt, G., (2006). “Quo vadis, design space explorer?” Artificial Intelligence for Engineering Design, Analysis and Manufacturing. Vol. 20, pp. 105-111.
Hartmann, T., Fischer, M., Haymaker, J. (2008). “Implementing information systems with project teams using ethnographic–action research,” Advanced Engineering Informatics, In Press.
Hauser, J., Clausing, D. (1988). “The House of Quality”. Harvard Business Review. Vol. May – June 1988, pp. 3-13
Hazelrigg, G. A., (1998). “A Framework for Decision-Based Engineering Design”. Journal of Mechanical Design. Vol. 20, Issue 4, pp. 653-658.
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Howard, Ronald A. (1966). "Decision Analysis: Applied Decision Theory" Proceedings of the 4th International Conference on Operational Research. pp. 55–77.
Kraft, B., Nagl, M. (2007). “Visual knowledge specification for conceptual design: Definition and tool support”. Advanced Engineering Informatics, Vol. 21, pp. 67–83.
Miller, L., (1993). “Concurrent Engineering Design: integrating the best practices for process improvement”. Society of Manufacturing Engineers.
Moran, T., Carroll, J. (1996). “Design Rationale: concepts, techniques, and use”. Lawrence Erlbaum Associates, Inc., Publishers.
Shah, J., Mäntylä, M. (1995). “Parametric and Feature-Based CAD/CAM: Concepts, Techniques, and Applications”. Wiley, John & Sons, Inc.
Welle, B., Haymaker, J., (2011). “Reference-Based Optimization Method (RBOM): Enabling Flexible Problem Formulation for Product Model-Based Multidisciplinary Design Optimization”. http://cife.stanford.edu/online.publications/TR195.pdf . Accessed January, 2011.
Woodbury, R., Burrow, A., (2006). “Whither Design Space?”Artificial Intelligence for Engineering Design, Analysis and Manufacturing. Vol. 20, pp. 63-82.
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7 Appendix The section contains the explanation of how the requirements not covered in section 3 of this paper were addressed.
Constraint No. 2 & 3 – Tower 2 Gross Area (hotel and serviced apartments)
The design scenario called for the footprints of both towers to be identical. Tower 2, however, was described by two programmatic constraints – hotel (40,000 sq m) and serviced apartments (50,000 sq m) summed to a total gross area of 90,000 sq m. Fig. 20 illustrates how the design architect rationalized constraints 2 and 3.
An “AND” relationship indicates that all six succeeding action items were required to address both constraints. Given the identical footprints for both towers, the first action and the dependent strategies and parameters from constraint 1 applied to constraints 2 and 3. “Calculate gross area” was the second action further decomposed into two strategies – “Hotel tier” AND “Residential tier”. Each strategy was addressed through a pair of parameters – “Single floor area”, an output parameter to be measured in the CAD model and dependent on the “Tower1 base length” and “Major arc radius” parameters, AND “Gross area”, an input parameter with a constant value. The third action, “Control number of floors”, was addressed through two output parameters – “Total number of hotel floors” AND “Total number of residential floors”. Both were calculated by dividing “Gross area” to “Single floor area” for the hotel and residential tiers respectively. The fourth action, “Calculate number of units”, was decomposed into two strategies – “Hotel tier” AND “Serviced apartments tier”, addressed through three output parameters – “Number of units north”, “Number of units south major arc”, “Number of units south minor arc”. The fifth action, “Calculate balcony length”, was introduced in response to “Create glass enclosed balconies as buffer zones” action addressing three goals introduced later in the following sections.
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Figure 4-20: Scenarios Model for constraints No. 2 & 3.
The fifth action was decomposed into three strategies – balcony length along “North perimeter”, in which “North balcony perimeter length” parameter applied globally because the tower’s north side footprint was a straight line (Figure 4-21a), “South major arc perimeter”, and “South minor arc perimeter”, in which the “South balcony major arc length” and “South balcony minor arc length” parameters were unique for every balcony (Figure 4-21b).
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a) b)
Figure 4-21: Diagrams illustrating how the balcony lengths were calculated balcony perimeter length on north side; b) balcony perimeter arc length on south sides.
The final action, “Control unit width”, was addressed by “Unit width” input parameter, which had its values constrained between 4.2 – 4.8m.
Constraint No. 4 – Maximum tower height to last inhabited floor
To address constraint 4 (Figure 4-22), the design architect proposed only one action – “Control tower height”, further decomposed into two strategies: “Individually” and “Globally”. The XOR relationship communicated that only one strategy had to be chosen given the mutual exclusiveness of these. To attain more flexibility, the architect chose the first strategy addressed through the following five parameters. “Tower 1 floor height” was an input parameter with values ranging between 3.0–3.6m (expert knowhow was used to determine this range), “Tower 1 height”, an output parameter calculated by multiplying “Tower 1 No. floors” from constraint 1 with “Tower 1 floor height”. Similarly, “Tower 2 hotel floor height” AND “Tower 2 residential floor height” were input parameters with values ranging between 3.0–3.6m and used to calculate the “Tower 2 height” output parameter.
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Figure 4-22: Scenarios Model for constraint No. 4.
Constraints No. 5, 6, 7 – North and South site setbacks, Maximum site coverage
The next three constraints were straightforward to rationalize (Figure 4-23). The “Setback north” and “Setback south” parameters (with values greater or equal to 12m and 3m respectively) were the only ones needed to address the site setback constraints. The design architect proposed three required actions describing the “Maximum site coverage” constraint: “Calculate site area”, addressed through “Site area” constant output parameter calculated by measuring the site area in CAD, “Calculate ground floor area of both towers”, addressed through “Tower 1 and 2 single floor area” output parameter, and “Calculate site coverage”, addressed through “Percentage of site coverage” output parameter.
Figure 4-23: Scenarios Model for constraints No. 5, 6, 7.
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Goal No. 1 – Maximize unit exposure to water
The design architect and mechanical engineer proposed three required actions to address the project’s most important goal (Figure 4-24). First, “Control unit orientation” was further decomposed into three required strategies – south, north, and city “facing units”. Each strategy was addressed by a “View vector” input parameter with the angle range determined by the architect based on the site’s perpendicular orientation to the water. Second, “Calculate percentage of units facing water” was addressed by the “Percentage of units facing the water” output parameter calculated through an algebraic expression captured in the parameter node. Third, “Control tower orientation” was further decomposed into two strategies – “Globally” OR “Each tower individually”. The design architect decided to have one input parameter “Tower rotation” with an angle ranging between 0-10 degrees applied to both towers.
Figure 4-24: Scenarios Model for Goal No. 1.
Goals No. 2 & 3 – Minimize direct sunlight in units and Minimize solar heat load
To address Goals 2 and 3, the design architect and mechanical engineer proposed eight required actions (Figure 4-25). “Control tower orientation”, “Control unit width” from Goal 1 AND “Control unit width” from Constraints 2 and 3 also addressed Goals 2 and 3. The fourth action, “Consider passive shading techniques”, was
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decomposed into four strategies. The first two indicated the requirement for top AND bottom balcony sections to be shaded. The architect proposed three output parameters determining the “Shaded section height” in Tower 1 and the hotel and residential tiers of Tower 2. The other two strategies offered a choice between two materials – reflective metal vs. fritted glass illustrated through an XOR relationship. The design architect chose the fritted glass strategy in support of two other goals – “Maximizing unit exposure to water” and “Sleek design”. The “Frit density” input parameter explicitly communicated the architect preferred frit range later used in daylight simulations. The fifth action, “Control balcony depth” was retracted soon after being proposed because of a conflict with “Control unit orientation” action from Goal 1, which already helped determine the balcony depth.
Figure 4-25: Scenario Model for Goals No. 2, 3.
The sixth action, “Introduce horizontal sliding panels for natural ventilation”, was addressed through “Sliding panel height” output parameter. The seventh action, “Control envelope inclination at each level”, was decomposed into two strategies – globally OR individually on “North, South, East, West walls”. The design architect
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decided on the second strategy in order to attain greater flexibility in exploring design options for the exterior envelope. The mechanical engineer proposed four required input parameters matching the building orientation with an inclination angle ranging between 1–8 degrees.
Goals No. 4 & 5 – Sleek design and Maximize exposure to prevailing wind
The design architect proposed two required actions to address the “Sleek Design” qualitative goal – “Create glass enclosed balconies” from goals 2 & 3 AND “Creating an all glass exterior” complementary action.
Figure 4-26: Scenario Model for Goals No. 4, 5.
The design architect and the mechanical engineer decomposed the last project goal into three required actions – “Control tower orientation” AND “Control half teardrop configuration” from Constraint 1 and Goal 1, AND “Calculate percentage of prevailing wind facing units”, assessed through “Percentage of total units facing prevailing wind” output parameter. The mechanical engineer proposed the last action after he determined the prevailing wind direction, information also used to write the formula for calculating the output parameter (Figure 4-26).
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