AN ABSTRACT OF THE DISSERTATION OF
Ding Liu for the degree of Doctor of Philosophy in Civil Engineering presented on May 15, 2019.
Title: Evaluating Industrialization Rate in Construction: A Quantification Model
Abstract approved:
______John A. Gambatese
The concept of construction industrialization, first raised in the 1960s, refers to the
transfer of on-site construction work to an off-site factory to improve quality and reduce
cost, time, and safety issues. In some countries, industrialization of construction
projects is highly recommended and promoted by governments and local construction
institutions to minimize waste and pollution. The industrialization rate in construction
is an important factor that is widely accepted to evaluate the level of industrialization in the industry. It is believed that construction industrialization rate (CIR) is associated with construction performance in terms of cost, schedule, safety, quality, and other measures. The construction industry currently utilizes volume of concrete use to evaluate the CIR, which is not necessarily accurate and convincible. No other formal method or model is available to objectively calculate the rate of construction industrialization. The author sets this knowledge gap as the point of departure and aims
to develop a formal model for quantifying the CIR value of projects. To combine all of
the units associated with the various construction operations and resources on a project,
the researcher utilizes energy expenditure as a means for determining the percentages
of human work and machine work. All activities and tasks could be measured with
energy expenditure; energy is the most commonly-used unit that could be applied to all
construction tasks, including both worker activities and machine activities. The
researcher evaluates energy expenditure by quantifying all construction tasks and the
associated energy expenditures for both on-site and off-site construction to record the
amount of energy expended by machines and by laborers. During the model creation
and validation process, the author utilized quantification, literature reviews, surveys,
site observations, video comparisons, and cross-reference spreadsheets as the research methods. By applying the model to sample case projects, the author confirmed the feasibility of applying the model, and was able to find and solve issues and defects during application. The findings from the research provide evidence and statistical measures to calculate the CIR, and provide project owners/developers and construction contractors with a quantitative means to evaluate and market their projects based on the extent of industrialization used to construct the projects.
©Copyright by Ding Liu May 15, 2019 All Rights Reserved
Evaluating Industrialization Rate in Construction: A Quantification Model
by Ding Liu
A DISSERTATION
submitted to
Oregon State University
in partial fulfillment of the requirements for the degree of
Doctor of Philosophy
Presented May 15, 2019 Commencement June 2019
Doctor of Philosophy dissertation of Ding Liu presented on May 15, 2019.
APPROVED:
Major Professor, representing Civil Engineering
Head of the School of Civil and Construction Engineering
Dean of the Graduate School
I understand that my dissertation will become part of the permanent collection of Oregon State University libraries. My signature below authorizes release of my dissertation to any reader upon request.
Ding Liu, Author
ACKNOWLEDGEMENTS
I would like to express my deepest appreciation to my advisor, Dr. John Gambatese: I could not make it to this point without your generous guidance and indispensable support. You were the advisor for my master program, and thank you for being my advisor during the past years, and will always be in my life. I am also grateful to the members of my committee for their patience and support in overcoming numerous obstacles I have been facing through my research.
I also wish to thank my family: my mom and my dad. Thank you, Zhenfang Tan and
Shanchen Liu, for sending me overseas to the United States to learn more and explore more. I am glad that I proved that you made the most correct decision.
Sincerely thanks to everyone in our research group, you guys are the best. I am glad and honored to join Gambatese Research Group and will miss the days we spent together drafting papers and brainstorming ideas.
I would like to express my sincere appreciation to all the friends I met here, I am happy to have you guys in my life. Special thanks goes to Yiye Xu, Ziyu Jin, Chuma Nnaji, and Ali Karakhan for being supportive colleague and friends. Thanks also to all the groups and individuals who supported and helped me during my Ph.D. program study.
TABLE OF CONTENTS
Page
1. INTRODUCTION ...... 1
2. LITERATURE REVIEW ...... 4
2.1 Construction Industrialization ...... 4
2.2 Construction Industrialization Rate (CIR) ...... 5
3. PROBLEM STATEMENT ...... 8
4. RESEARCH OBJECTIVES ...... 12
5. RESEARCH DESIGN ...... 13
6. MANUSCRIPT #1 ...... 20
6.1 ABSTRACT...... 21
6.2 INTRODUCTION ...... 22
6.3 CURRENT PRACTICE ...... 25
6.4 RESEARCH GOALS AND METHODS ...... 26
6.5 RESULTS AND ANALYSIS ...... 31
6.6 RESEARCH LIMITATIONS ...... 35
6.7 CONCLUSIONS AND RECOMMENDATIONS ...... 36
6.8 REFERENCES ...... 37
TABLE OF CONTENT (Continued)
Page
7. ENERGY EXPENDED BY MACHINES PERFORMING ON-SITE
CONSTRUCTION TASKS ...... 40
7.1 INTRODUCTION ...... 40
7.2 METHODOLOGY ...... 41
7.3 FINDINGS ...... 41
7.4 RESULTS AND CONCLUSIONS...... 45
7.5 REFERENCES ...... 47
8. MANUSCRIPT #2 ...... 49
8.1 ABSTRACT...... 50
8.2 INTRODUCTION ...... 51
8.3 BACKGROUND AND LITERATURE REVIEW...... 53
8.3.1 Extent of Off-site Construction ...... 55
8.3.2 Levels of Automation (LOAs)...... 56
8.3.3 Energy Measurement ...... 58
8.4 METHODOLOGY ...... 60
8.4.1 Research Model ...... 60
8.4.2 Survey of Concrete Plant Personnel ...... 61
8.4.3 Energy Expended by Laborers ...... 63
TABLE OF CONTENT (Continued)
Page
8.4.4 Energy Expended by Equipment ...... 65
8.4.5 Levels of Automation in Construction ...... 66
8.5 DATA ANALYSIS AND RESULTS ...... 69
8.5.1 Demographic Information...... 69
8.5.2 Variable Explanation ...... 70
8.5.3 Energy Expenditure by Workers ...... 71
8.5.4 Energy Expenditure by equipment ...... 72
8.6 LIMITATIONS ...... 74
8.7 CONCLUSIONS AND RECOMMENDATIONS ...... 75
8.8 DATA AVAILABILITY STATEMENT ...... 77
8.9 ACKNOWLEDGEMENT ...... 77
8.10 REFERENCES ...... 77
9. MANUSCRIPT #3 ...... 82
9.1 ABSTRACT...... 83
9.2 INTRODUCTION ...... 83
9.3 LITERATURE REVIEW ...... 85
9.3.1 Estimating Human Energy Expenditure (BMR & PAR) ...... 85
9.3.2 Lists of Construction Design Elements and Tasks (On-site and Off-site) ...... 87
TABLE OF CONTENT (Continued)
Page
9.3.3 Levels of Automation ...... 90
9.5 DATA COLLECTION AND RESULTS ...... 94
9.5.1 Survey Results ...... 94
9.5.2 Site Observations ...... 98
9.6 MODEL CREATION AND VALIDATION ...... 98
9.6.1 Obstacles and Assumptions of Model Creation ...... 100
9.6.2 Model validation ...... 105
9.7 MODEL APPLICATION ...... 111
9.7.1 Model Interface ...... 112
9.7.2 Application Case Study 1 ...... 115
9.7.3 Application Case Study 2 ...... 117
9.7.4 Findings from Application ...... 119
9.8 LIMITATIONS ...... 122
9.8.1 Small data size ...... 122
9.8.2 Productivity Issues ...... 123
9.8.3 Inaccurate Energy Estimating in Precast Concrete Plant ...... 124
9.9 CONCLUSIONS AND RECOMMENDATIONS ...... 124
9.10 REFERENCES ...... 126
TABLE OF CONTENT (Continued)
Page
10. DISCUSSION ...... 130
10.1 Answers to research questions ...... 130
10.2 Statistical methods statement ...... 134
11. CONCLUSIONS...... 136
11.1 Manuscript #1 ...... 136
11.2 Manuscript #2 ...... 137
11.3 Manuscript #3 ...... 139
11.4 General Conclusions ...... 140
11.5 Contributions to the Body of Knowledge ...... 141
12. LIMITATIONS ...... 143
13. FUTURE RESEARCH RECOMMENDATIONS ...... 144
13.1 Model Improvement ...... 144
13.2 Potential Relationships ...... 145
13.3 CIR Evaluation and Guidance ...... 146
14. BIBLIOGRAPHY ...... 147
15. APPENDIX ...... 152
15.1 Appendix A IRB Approval Letter of Research Surveys ...... 152
TABLE OF CONTENT (Continued)
Page
15.2 Appendix B Survey Questionnaire – On-site Construction Task Survey ...... 154
15.3 Appendix C Survey Questionnaire – Off-site Precast Concrete Plant Survey ...... 160
15.4 Appendix D Hand-written Notes and Calculations for On-site Equipment Identification
(Scanned) ...... 167
15.5 Appendix E Screenshots of CPQM model ...... 172
15.6 Appendix F On-site Construction Tasks List Used in CPQM (Systems and Design
Elements included) (Construction tasks excluded) ...... 179
LIST OF FIGURES
Figure Page
Figure 1.1 Hypothesis of potential correlation between CIR and Construction performance ...... 3
Figure 5.1 Construction Task Categorization ...... 13
Figure 5.2 Manuscript #1 research flow and data diagram ...... 15
Figure 5.3 Manuscript #2 research flow and data diagram ...... 16
Figure 5.4 Manuscript #3 Research Flow and Data Diagram ...... 17
Figure 5.5 Research Strategy ...... 18
Figure 6.1 Basil Metabolic Rate (BMR) and Physical Activity Ratio (PAR) ...... 26
Figure 6.2 Work Experience of Survey Participants (n = 15) ...... 32
Figure 8.1 Categories of Construction Tasks ...... 53
Figure 8.2 CPQM model off-site part ...... 62
Figure 8.3 Unit Power consumption vs. Automation Level ...... 73
Figure 9.1 Research Framework (Manuscript #3) ...... 92
Figure 9.2. Survey results: Barriers to performing industrialization ...... 97
LIST OF TABLES (Continued)
Table Page
Figure 9.3 CPQM Model Simplified Strategy ...... 99
Figure 9.4 Video Analysis Demo...... 108
Figure 9.5 Video Analysis with Identified Timeline Frame ...... 109
Figure 9.6 PAR validation (on-site concrete process) ...... 110
Figure 9.7 PAR validation (off-site concrete process)...... 111
Figure 9.8 CPQM model - Input page ...... 113
Figure 9.9 CPQM model - Output page ...... 114
Figure 9.10 Range Photo of Case Study 1 (OSU 2014) ...... 116
Figure 9.11 Output of case study 1 application ...... 117
Figure 9.12 Range Photo of Case Study 2 (OSU 2015) ...... 118
Figure 9.13 Output of case study 2 application ...... 119
LIST OF TABLES
Table Page
Table 6.1 Construction Process Categories ...... 24
Table 6.2 Daily Activity and Construction Activity Relationship ...... 29
Table 6.3 Top 5 Construction Tasks with Highest/Lowest PAR value ...... 33
Table 7.1 List of On-site Construction Equipment ...... 44
Table 8.1 Levels of Off-site construction for buildings ...... 56
Table 8.2 Precast Concrete Construction Tasks (wet and dry) ...... 59
Table 8.3 Levels of Automation (LOA) Analysis and Conversion to Construction .. 67
Table 8.4 PAR value for off-site precast concrete tasks ...... 72
Table 9.1 Construction tasks of precast concrete (wet and dry mixing methods)
(CONQUAS 2018)...... 89
1
1. INTRODUCTION
Since the first industrial revolution began in Britain in the late 18th century, nearly all industries got on the fast track of developing with mechanization. As one of the industries with the highest levels of laborer intensity, the construction industry has also benefited from the explosive growth of mechanization in human society. However, this rate of mechanization has been at a relatively lower rate of development in construction compared to other industries, such as manufacturing and agriculture. Over the past decades, researchers and practitioners in construction have been working hard to solve the issues of labor utilization and machine use to improve construction industry performance, and significant improvements have been achieved in the areas of construction safety (OSHA 2019), quality (Wang, et al. 2015), budget control (Tahir, et al. 2018) and schedule planning (Kong, et al. 2018). In this new era of technological revolution, new industries, technologies, and automated systems were developed, and have caused a huge impact on the traditional industries. Far-reaching and critical issues and obstacles appeared among all industries, and forced the practitioners to make changes. Construction is one industry among the traditional industries that is faced with critical challenges.
Currently, the construction industry is faced with critical challenges such as skilled labor shortages (Goh and Goh 2019), safety issues (OSHA 2019) (Rubio-Romero,
Suarez-Cebador and Abad 2014), environmental sustainability (Wang, et al. 2019), and technology adoption (Darko, et al. 2017). Multiple solutions have been proposed, created, and applied to address the challenges. Lean construction, building information 2
modeling (BIM), automation systems, and other techniques are now widely accepted and being used on construction sites to produce continuous improvements. Among the potential solutions, construction industrialization (CI), firstly raised in the early 1960s, is also becoming more popular over the years. In some countries, industrialized or modularized construction is highly recommended and promoted by the governments and local construction institutes to minimize waste and pollution. The benefits of CI are more than just minimizing waste and pollution. With appropriate planning, using industrialized modular components could reduce the schedule of a project, improve construction safety and quality (Rubio-Romero, Suarez-Cebador and Abad 2014), and may reduce the overall budget of a project.
However, there are concerns and constraints about implementing construction industrialization. A large amount of initial investment, supply chain availability, political support, and availability of trained workers are obstacles for contractors to consider before deciding to implement CI. It is clear that there are pros and cons for implementing this construction method from the perspective of the constructor. The assumption (shown in Figure 1.1) was made based on existing models, such as Yerkes-
Dodson Law (Yerkes and Dodson 1908) and diminishing returns (Samuelson and
Nordhaus 2001). The models indicated that all production processes, adding more of one factor of production while holding all others constant, will at some point yield lower incremental per-unit returns. Hence, the researcher assumed that there are correlations between the construction industrialization rate (CIR) and construction performance, demonstrated in Figure 1.1. If current construction industrialization is at the rate in the yellow zone in Figure 1.1, how far shall we push the industrialization 3
rate to get the optimal construction performance? Or, if the CIR is currently in the red zone, how much should we pull backward to get a better return on construction performance. The first step for testing the hypothesis of the potential correlation between CIR and construction performance is to find a way to evaluate the rate of construction industrialization, and it is also the point of departure for this research study.
Figure 1.1 Hypothesis of potential correlation between CIR and Construction performance
4
2. LITERATURE REVIEW
2.1 Construction Industrialization
Construction industrialization refers to the transfer of construction processes from the
construction site (on-site) to off-site factories (Gibb 1999). The intent of construction
industrialization is to improve the key project objectives of time, cost, quality,
environment, and safety (Gibb 1999). The concept of industrialized construction was
identified as early as the 1960s, and was established for several purposes: increasing
labor productivity, replacing manual labor with machines, accelerating the pace of
construction, and others (Goh and Loosemore 2016). Today, the idea of construction
industrialization is much more complex, it contains off-site production (Nadim and
Goulding 2011), off-site manufacturing (Hampson and Brandon 2004), prefabrication
(Tam, et al. 2007), modular construction (Zhang, et al. 2016), industrialized construction (Ji, et al. 2017), and automation (Ha, et al. 2002). Large amounts of existing research studies have focused on these topics within the construction industry, and discussed the benefits and potential risks of implementing industrialized construction.
Previous researchers have defined the hierarchy of construction industrialization (Gibb and Isack 2003). According to Gibb and Isack (2003), five levels of industrialization can be defined, from the lowest level of traditional building to on-site prefabrication, off-site prefabrication, pods (pre-assembled units, such as toilets or bathrooms), and finally the highest level that involves complete modularization. Among the five levels of industrialization, it is reported that the “pods” level is increasingly being used in 5
many countries, particularly in the residential building market (Goh and Loosemore
2016). Though some of the projects used construction industrialization rate or industrialization level as the metric to measure the amount of work that is conducted
off-site (Goh and Loosemore 2016) (Ji, et al. 2017), none of them mentioned how the
rate of construction industrialization was calculated.
2.2 Construction Industrialization Rate (CIR)
Construction industrialization rate (CIR) is considered to be the metric that can be used
to evaluate how much of a construction project was pre-fabricated or modularized in a
factory. Current practice for measuring CIR is based on the percentages of concrete
consumed off-site, which is not necessarily accurate and convincible. Because various
projects may have different requirements on concrete strength and rebar, using
percentage of concrete as the only merit is biased in estimating CIR values. Based on
the current extent of research, no formal calculation method or model is available for
evaluating CIR. Furthermore, no current research studies have mentioned ways of
measuring construction industrialization. The researcher sets this knowledge gap as the
point of departure for the present study and aims to develop a formal model for
quantifying the CIR value of projects.
To create a quantification model, a common unit that could describe all construction
elements of a project and the work performed on the project is necessary. In complex construction projects, multiple units are used to measure different elements. For example, the industry uses cubic yards (volume) to measure the amount of concrete, uses square feet (contact area) to measure the amount of formwork, and uses tons 6
(weight) to measure the amount of rebar and steel. Thus, determining a way to combine
all of the units is the first issue to solve as part of creating the model.
Originally, the researcher brainstormed several units that could be used to evaluate all
construction tasks, including work-hours expended, energy expenditure, and task time
scheduled. Task time scheduled was the first abandoned idea since different tasks incur
different amounts of time and even the same task on different projects may need
different amounts of time to complete due to the volume and complexity of the projects.
Work-hour expended was also initially considered to be a good unit to evaluate projects.
Percentages of industrialization could be measured by recording the work-hours spent
for on-site and off-site construction tasks. However, larger projects need more work-
hours to complete, but this large number of hours does not influence the percentages or
ratio between on-site work-hours and off-site work-hours spent. The critical issue of
using this unit is that work-hour is largely dependent on worker productivity, while
productivity is hard to measure for different tasks and projects.
Energy is the ability to do work, and is a measure that could be used to evaluate
everything from walking to sending astronauts into space (EIA 2018). All construction
design elements and tasks could be evaluated with respect to energy expenditure for both work performed by laborers and work performed by equipment. Since energy was firstly defined via work, the use of energy to evaluate work on a construction project is a natural fit. Also, the international system unit for energy is the same as the unit of work – joule (J). There are multiple units that are used to express energy, such as kilowatt-hour (Kwh) for electrical power, calories (Cal) for food rating and human 7
energy rating, British Thermal Unit (BTU), and horsepower, all of which can be converted to joules (Silverman 2019).
8
3. PROBLEM STATEMENT
In detailed searches of the ASCE library, Elsevier database, OSU library catalog, and
Google scholar website, only a few papers and research studies used “construction
industrialization rate” and “off-site construction rate” as the keywords, and the concept
of construction industrialization rate was rarely mentioned. Several papers mentioned
off-site construction optimization (Blismas, Pasquire and Gibb 2006; Arashpour,
Wakefield and Blismas, et al. 2014; Arashpour, Wakefield and Abbsi, et al. 2016). In these papers, the authors propose that there is a balance between on-site construction processes and off-site construction processes, which implies that a higher rate of off- site processes might not result in better construction performance.
To determine the optimal balance point, there may be a rate or percentage value that can be used to perform the evaluation and measurements. Current studies identified all construction processes as on-site processes and off-site processes. The industrialized construction portion mainly indicates the off-site processes, so the industrialization rate is basically the percentages of the processes that are performed off-site. To better evaluate the CIR, a quantification model is needed.
To create a quantification model based on energy expenditure and evaluate each of the construction design elements and tasks, a complete list of all construction tasks should be identified. RS Means is North America’s leading source of construction cost information provider (Gordian 2019). Based on its past publications, a full list of on- site construction tasks was generated, which contains more than 100 design elements with approximately 1000 construction tasks (Dharmapalan, et al. 2015). The researcher 9
decided to use this list as the main resource for future computing of the CIR, and name this model as the construction process quantification model (CPQM). Before starting the research activities, multiple research questions and hypothesis were generated to plan and guide the research. The research questions and hypotheses are described below.
Q1. What unit can be used as the common unit for evaluating construction tasks?
Hypothesis: The CIR of construction tasks can be assessed with energy
expenditure, and energy expenditure is measurable for construction tasks.
Basis: It is widely known that energy expenditure can be used to describe
human and machine activities. Using energy as the common unit for evaluating
construction tasks, however, has not been tested based on prior research studies.
To determine if energy expenditure is feasible for measuring the CIR of
construction tasks should be the first research question to answer.
Q2. How should construction tasks be categorized for determining the CIR?
Hypothesis: All construction tasks can be categorized into four parts, namely
artisanship (on-site by laborer), automation (on-site by machine), pre-
fabrication (off-site by laborer), and industrialization (off-site by machine).
Basis: Current studies categorize industrialized construction based on location
of the work (on-site and off-site) (Arashpour, Wakefield and Abbsi, et al. 2016),
and off-site construction processes are believed to be beneficial to construction
performance. To evaluate CIR, determining how construction tasks could be
categorized based on location of the work (on-site and off-site) and by how the 10
work is completed (laborers and machines) is one of the important research
steps.
Q3. What method should be used to estimate construction workers’ energy expenditure
while performing different tasks?
Hypothesis: Workers’ energy expenditure can be measured with wearable
devices or using mathematical calculations.
Basis: As the main process of implementing energy expenditure in the
quantification model, the methods for evaluating construction laborer energy
expenditure is the decision that the researcher needs to make. To measure
construction laborer energy expenditures while performing different
construction tasks, the researcher needs to identify the most feasible method for
this research study.
Q4. What construction elements or components can be pre-fabricated off-site in factories?
Hypothesis: All components of construction contain off-site production
processes, and can be pre-fabricated or pre-assembled off-site in factories.
Basis: To evaluate what types of construction tasks could be conducted off-site
to replace on-site processes, it is important to correctly identify the construction
elements or components that could be pre-fabricated off-site in factories. The
purpose of this research question is to determine which construction process
could be performed either on-site or off-site. Answering this research question 11
will provide guidance in determining which construction tasks are to be
included in the CPQM model.
Q5. How can the CPQM model be displayed or used with a user-friendly interface?
Hypothesis: The completed model can be used and applied to real projects with
easy-access interfaces for users.
Basis: The completed CPQM model is designated to be utilized in the
construction industry for estimating the CIR. Therefore, a user-friendly
interface is extremely important. The question of how and in which software to
display the CPQM model is the main focus that needs to be determined.
Q6. Is there a way to validate the CPQM model? How can the feasibility and validity of the model be tested?
Hypothesis: Feasibility and validity tests can be performed after the model is
completely created. The tests can be performed by using real project application
and real project video comparison.
Basis: After the model is completed, what kinds of methods could be used to
validate the model? The researcher would like to identify methods to make sure
the CPQM model is feasible and validated. Consequently, a check process
should be designed after the model is completed.
The author wishes to answer all the research questions and prove the hypothesis during the research processes. And based on the processes of solving research questions, the author designed the research methodology in the following Chapter four. 12
4. ESEARCH OBJECTIVES
The overall goal of this research study is to create a quantification model, based on energy expenditure, to evaluate the rate of construction industrialization. The purpose of developing and applying the construction process quantification model (CPQM) is to provide scientific support for CIR evaluation, which will provide suggestive guidance to future construction industry project teams, and serve as one of the solutions to the potential construction challenges. With the quantification model, the researcher would like to categorize energy expenditure by different construction tasks into four
defined construction process quantification categories (artisanship, automation, pre-
fabrication, and industrialization), which would be used to evaluate the rate of
construction industrialization, together with the rates of artisanship, automation, and pre-fabrication.
It is expected that by the end of this research study, the researcher would be able to answer all of the research questions and determine the solutions to the different obstacles and constraints associated with creating the CPQM model. Potential defects and assumptions are to be recorded for model validation and inspection, or for future model improvement. The developed model would be used to conduct a series of analyses of construction performance, such as cost, schedule, safety, and quality. For the construction industry, the CPQM model is expected to be one of the major tools for estimating CIR and guiding future projects.
13
5. RESEARCH DESIGN
The researcher decided to implement three research steps to reach the overall goal.
Three steps are presented in the format of three individual manuscripts, and designed
to be combined as the main content of this research dissertation.
Based on an extended literature review, the researcher identified the research gap: no
formal method or model for CIR evaluation exists. With the problem stated in previous
chapters, it is assumed that all construction processes could be categorized into four
disciplines based on the location of the work, either on-site or off-site, and the manner in which the work is completed, either by laborers or machines (see Figure 5-1). Based on this assumption, the researcher analyzed the on-site energy expenditure and off-site energy expenditure separately in two steps.
Figure 5.1 Construction Task Categorization For the first manuscript (Manuscript #1), the goal is to test the assumption of the feasibility to use energy as the common unit to quantify construction tasks, and answer the first three research questions. In the first manuscript, identifying how energy expenditure can be measured is needed, along with analyzing the energy expended for 14
on-site construction tasks, by both laborers and machines. See Figure 5.2 for the
research and data diagram of Manuscript #1.
The second manuscript (Manuscript #2) focuses on the analysis of off-site construction tasks, and aims to evaluate energy expenditure by laborers and machines for off-site tasks. To determine which construction tasks should be included in the CPQM model in CIR evaluation, the researcher will try to answer the fourth research question. During the off-site construction task analysis, the researchers will focus on the selected construction design elements and work processes. Data collection and analysis will be conducted with different research methods (survey, interview, site observations, etc.), and the energy evaluation method will be similar to that used in the first manuscript to maintain consistency in the results. The research flow and data collection are shown in
Figure 5.3.
The last manuscript (Manuscript #3) combines the results from the first two manuscripts, and uses the data collected to build the CPQM model. Possible validation and feasibility check are conducted in the third manuscript. The possible improvement, defects, and obstacles identified during the application phase are also recorded.
Potential future application and suggestive guidance are also listed in the third manuscript. See Figure 5.4for a diagram showing the research flow and data requirements in Manuscript #3.
The overall research design for the entire study that integrates Manuscripts #1 – #3, is shown in Figure 5.5. 15
INPUT: Quantity (Units) of Design Element + Equipment type and model used
Survey of Literature Construction Literature Review Personnel Review
List of Construction Worker hour Possible On-site Tasks and Design distribution for Count all worker activities that require assistant of Equipment/Machines Equipment/ Element each work task Machines
Man-hour per UNIT Energy Consumption Total Energy for each Design (unit: Man-Hour/Unit) per UNIT for each consumption for Time required for Element task each task Equipment/ Machines per UNIT (unit: Energy/Man-Hour) for each RS Means and construction task Total Energy Literature Review Energy Consumption PAR for each Consumption for per MAN-HOUR for work task each Design each work task Total Energy Total Energy Define basic Element Consumption Consumption for Relate Construction for entire each Design Worker activities Total Energy Project Element BMR (Basal Consumption for Metabolic Rate) entire Project Daily Life PAR for basic activities with construction related PAR worker activities On-site On-site Labor Equipment/ Artisan Machines Literature Literature Automation Review Review
Figure 5.2 Manuscript #1 research flow and data diagram 16
PAR for basic INPUT: construction Quantity (Units) of off-site worker activities concrete use
Off-site PAR for each Energy Total Energy Off-site Labor Equipment/ working crew Consumption per Consumption for Pre-Fabrication Machines workers in Precast MAN-HOUR for entire Project Industrialization Concrete plant each work crew Daily electrical (unit: Energy/Man-Hour) power consumption Energy of entire plant Worker hour Total Energy Hours Required to Consumption per (Kwh/hour) distribution for each consumption for finish the task UNIT for each work task each worker (Hours) group worker Daily electrical power consumption of equipment Productivity of the (Kwh/hour) Survey of PC plant Construction off-site (cy/hour) Daily electrical Precast Concrete power consumption plant of entire plant (Kwh/hour)
Figure 5.3 Manuscript #2 research flow and data diagram 17
Off-site Energy Consumtpion
CIR = (Off-site energy consumption)/ Literature Review Surveys Off-site (Entire energy Consumption)
On- or Off-site On-site energy On-site Construction Tasks task? Consumption PAR value for construction tasks
Unit task work hour requirement (work- Worker energy to Equipment energy hour/unit) be categorized to be categorized Energy consumption performing unit tasks (Calories/unit) Construction Worker Quantity of Types of Average BMR each task Equipment used (Calories/day)
Unit Energy = BMR*PAR*work-hour/unit Equipment Types, USER Models and Wattages INPUT
Figure 5.4 Manuscript #3 Research Flow and Data Diagram 18
Manuscript #2 Manuscript #1 Energy Consumption of off-site activities Energy Consumption of on-site activities (both Laborers and Equipment in pre-cast (both Laborers and Equipment) concrete plant) [Answers Research Questions Q1, Q2, Q3] [Answers Research Question Q4]
On-site Laborers Off-site Laborers Artisanship Prefabrication
On-site Equipment Off-site Equipment Automation Industrialization
Manuscript #3 Create Construction Process Quantification Model (CPQM) based on Manuscript #1 and #2. Validate and apply CPQM model [Answers Research Questions Q5 and Q6]
Contribution: Processes that allow construction industry to assess construction industrialization rate
Figure 5.5 Research Strategy For the research methodologies in different manuscripts, the author planned to collect
data from extended literature review and check if past research studies have related
data. After defining the missing data, the author would like to conduct surveys or use
wearable devices for energy expenditure collecting. Site observation and video recording for both construction sites and off-site factories will be conducted with the permissions. To display the completed model with built-in database, the author would 19
like to use cross-referenced Excel spreadsheets for this study. If further functional required, more advanced coding method might be applied.，
The scope of this research thesis is set to be the energy expenditure for on-site and off- site construction by both laborers and equipment. Other energy expended for a project, such as construction workers’ energy expenditure while driving to/from work, and energy expended for transportation of construction design elements or modules from plants to construction sites, are not included in this study.
From Chapter 6 to Chapter 9, three manuscripts will represent three steps of creating, validating, and applying the CPQM model.
Note: The content of Chapters 1, 2, 3, 4 and 5 presents the overall introduction, literature reviews, problem statement, research objectives, and research design of this research study. Detailed and individual sections are illustrated in each of the manuscripts in Chapters 6, 7, 8 and 9. 20
6. MANUSCRIPT #1
ENERGY EXPENDITURE BY CONSTRUCTION WORKERS FOR ON-SITE ACTIVITIES
Ding Liu and John Gambatese
Modified paper from Proceedings of Construction Research Congress 2018 New Orleans, LA April 2-4, 2018
21
6.1 ABSTRACT
The concept of construction industrialization, first raised in the 1960s, refers to the
transfer of on-site construction work to an off-site factory to improve quality and reduce
cost, time, and safety issues. In some developing countries, industrialization of
construction projects is highly recommended and generalized by governments and local
construction institutions to minimize waste and pollution. However, there are concerns
and constraints associated with applying this construction method. To test if
industrialized construction projects have lower cost, shorter duration, fewer safety
issues, and better quality, the first step is to determine how to calculate the
industrialization rate. Up to now, there is no formal method available to calculate the
rate of construction industrialization. The overall goal of the study is to develop such a
method. To unite all of the units associated with the many construction operations and
resources on a project, the researchers utilize energy expenditure as a means for determining the percentages of human work and machine work. The researchers evaluate energy expenditure of on-site construction workers based on surveys and literature reviews, and evaluate energy expenditure of off-site concrete plants based on factory observations and data collection. The results of the analyses show that for construction activities operated on-site, construction workers consume different amounts of energy while processing different construction tasks. Comparing similar tasks between on-site and off-site operations, workers consume less energy in a factory than on a construction site. The findings from the research provide evidence and statistical measures to calculate the construction industrialization rate, and provide project owners/developers and construction contractors with quantitative means to 22
evaluate and market their projects based on the extent of industrialization used to construct the projects.
6.2 INTRODUCTION
The concept of construction industrialization, first raised in the 1960s, refers to the transfer of on-site construction work to an off-site factory to improve quality and reduce cost, time, and safety issues. In some developing countries, industrialization of construction projects is highly recommended and generalized by government and local construction institutions to minimize waste and pollution. However, there are barriers to construction industrialization implementation. According to research in Malaysia, the main barriers can be summarized into five main areas:
• Cost and Finance
• Skills and Knowledge
• Project delivery and supply chain
• Perception of clients and professionals, and
• Lack of government incentives, directives, and promotion
Additionally, the researchers visited several off-site pre-cast concrete plants in the
United States, and interviewed some experienced engineers on construction industrialization. Dan Serra is an experienced project engineer from Knife River Pre- cast Concrete, who has been working there since he graduated. He told the researchers, that one of the main barriers of implementing construction industrialization is that the cost is higher than on-site processes, there are also limits of loads, size and weight while transporting modules. 23
Normally, construction industrialization refers to those construction tasks that are conducted off-site in manufacturing facilities. For the present study, all construction tasks and design elements are organized into more detailed categories with respect to where the work takes place and what/who performs the work.
According to the operation location, construction procedures can be categorized as on- site processes and off-site processes. In addition, all processes are performed either by human laborers or by machines. Based on those categories, all construction procedures can be categorized as either Artisanship, Automation, Pre-fabrication, or
Industrialization as shown in Table 1.
• Industrialization – Defined as work and modules that are pre-fabricated off-
site using primarily machines and equipment (such as an automated production
line) and minimal human labor.
• Pre-fabrication – Refers to the work and modules that are pre-fabricated off-
site, using mostly human laborers and minimal assistance from machines and
equipment.
• Automation – Defined as work and modules that are fabricated and processed
on-site using machines and equipment to assist during the entire construction
and fabrication process.
• Artisanship – Applies to most of the current working methods conducted on-
site. Artisanship includes work in which human laborers do most of the on-site
work and machines/equipment may be utilized to assist the laborers to conduct
the work.
24
Table 6.1 Construction Process Categories
Location Off-site On-site Machines Industrialization Automation Resource Humans Pre-fabrication Artisanship
To calculate the construction industrialization rate, a common unit is required to perform the calculations. Energy is defined as “the strength and vitality required for sustained physical or mental activity, or power derived from the utilization of physical or chemical resources, especially to provide light and heat or to work machines”
(Google dictionary). Every process of work performed by machines or by humans can be measured in the unit of energy. Energy expenditure calculations should be based on all on-site and off-site construction tasks and design elements.
• BMR–Basal Metabolic Rate (BMR) is the number of calories required to keep
your body functioning at rest. BMR is also known as your body’s metabolism;
therefore, any increase to your metabolic weight, such as exercise, will increase
your BMR.
• PAR–The physical activity level of an individual can be determined by
assigning physical activity ratios (PAR) to different activities. The PAR is the
ratio of the energy expended in a particular activity and the BMR, and is thought
to be independent of body weight.
For this paper, the main focus is on the on-site construction tasks and measuring the energy expended by construction workers as they perform the work on-site. 25
Understanding the energy expended by workers on-site is also the first step in developing a construction industrialization quantification model.
6.3 CURRENT PRACTICE
To convert all construction tasks to energy, there are two origins that should be considered: construction tasks and unit energy expenditure. This section describes current practice with respect to construction tasks and design elements, and introduces the relationship between human activity level and energy expenditure.
Design elements are defined as different construction activities under each construction systems/categories, and construction tasks are defined as the tasks that need to be proceeded to complete each design element. For instance, under the construction category of structure frame, there are total 12 different design elements, including steel h-pile, steel pipe piles, precast concrete piles, bored concrete piles, and 8 more. To complete different design elements, the construction tasks vary. A good example is that comparing between steel pipe piles and precast concrete piles, the task of “removal of soil, cleaning of pipe before concreting” is not necessary for design element “precast concrete piles”.
Basal Metabolic Rate (BMR), also known as resting metabolic rate (RMR), is the largest component of the daily energy budget in most human societies and, therefore, any increases in RMR in response to exercise interventions are potential of great importance. BMR accounts for 50 to 80 percent of the energy used each day, which is the energy a body burns just to maintain functioning at rest.
According to ABC News, there are 10 factors that affect BMR and metabolism. Those factors are: muscle mass, age, body size, gender, genetics, physical activity, hormonal 26
factors, environmental factors, drugs, and diet. For formal BMR calculation, only
weight, height, age, and gender are needed. Created by James Arthur Harris and Francis
Gano Benedict, the BMR formula (also known as the Harris-Benedict equation) was first published in 1919 by the Carnegie Institution of Washington. The formula later was revised to improve its accuracy by Mifflin et al. (1990).
The number of calories burned each day is calculated by multiplying the basal metabolic rate times the physical activity level (PAL) or physical activity ratio (PAR).
PAR is the “energy cost” of an activity, expressed as a multiple of BMR. The value of
PAR differs for different activities. A PAR value of 1.0 refers to the resting metabolic rate and is also the smallest PAR value. As the intensity of the activity increases (e.g., more energy expended), the PAR value also increases. The range of PAR values extends from 1.0 to 8.0 times the BMR or even higher for very intense activities. Figure
6.1 illustrates the correlations between BMR and PAR.
Figure 6.1 Basil Metabolic Rate (BMR) and Physical Activity Ratio (PAR)
6.4 RESEARCH GOALS AND METHODS
Based on the current practice-related research studies, the goals of the present research are to determine how construction workers consume energy while performing different 27
construction tasks, and how much energy (measured in joules or calories) they consume while performing the tasks.
All of the construction tasks were referenced from the Structural task list and MEP task list developed from previous research. The construction tasks were cited from the RS
Means construction database with work-hours per unit required. All of the tasks were divided into eight overarching categories: Foundations and Footings, Structural frame,
Low slope roofing, Exterior enclosure, Interior construction, Mechanical system,
Electrical system, and Plumbing system. The construction tasks for each design element are listed and sum up to nearly one thousand detailed procedures.
For each design element under the construction tasks, the unit(s) is attached (e.g., for steel H-Piles, the unit is vertical linear foot). The number of worker-hours required to construct each unit of the design element is also listed (e.g., for steel H-Piles, the number of worker-hours required to construct one vertical linear foot of the work is
0.113 worker-hours).
Based on current research on construction tasks, the researchers combined existing tables listing Structural and MEP tasks, along with a spreadsheet that describes all of the construction tasks and design elements in a typical commercial building in eight different categories. The spreadsheet included more than 100 design elements and more than 900 construction tasks that could possibly cover all processes for an entire building construction project.
From an energy perspective, according to research by the Center for Construction
Research and Training (CPWR), the average age of construction workers in production occupations was 40.2 years in 2010. The average height and weight for an American 28
male are 69.2 inches (175.8 cm) and 195.7 pounds (88.77 kg), respectively; and the
average height and weight for an American female are 63.7 inches (161.8 cm) and
168.5 pounds (76.43 kg), respectively. Based on those statistics, the BMR value for
men and women can be calculated with the Harris-Benedict formula. An average BMR for a male construction worker is 1,790 calories/day (7.49 Megajoules/day), and 1,414
calories/day (5.92 Megajoules/day) for a female construction worker.
Assuming the operation time unit of each activity is hourly, the next step is to obtain
the hourly energy expenditure while operating different construction tasks. The
following formula (Equation 6.1) may be utilized:
( / ) = . / (6.1) 𝐵𝐵𝐵𝐵𝐵𝐵 𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐 𝑑𝑑𝑑𝑑𝑑𝑑 𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸 𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐 24 ℎ𝑟𝑟𝑟𝑟 𝑑𝑑𝑑𝑑𝑑𝑑 ∗ 𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶 𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡 𝑃𝑃𝑃𝑃𝑃𝑃 In Equation 6.1, the unit of Energy expenditure for a human would be calories, which
is one of the units that represents energy, and could be easily transformed into other energy units. Construction task PAR represents the ratio of energy expended while operating different construction tasks. A value of 24 hours/day is used in the equation because the unit of BMR is calories per day (24 hrs). If construction work hours are used to accomplish all calculations, the unit has to be turned into energy expended per hour.
According to the report titled “Human energy requirements”, the daily physical activities can be arranged and categorized into three levels, (1) Sedentary or light activity lifestyle, (2) Active or moderately active lifestyle, and (3) Vigorous or vigorously active lifestyle. Based on the basic use of the energy expenditure calculation, only several of the daily activities, listed in Table 6.2, were used. Table 6.2 lists some of the main daily activities from the report. The related construction activities cover 29
nearly all possible activities that construction workers do while operating on-site.
Energy Cost PAR is the ratio of energy cost relative to BMR/RMR. Take “Driving” as an example. This activity consumes twice (PAR = 2.0) as much energy as “resting without thinking of anything”. In this case, it is assumed that “driving car to /from work” and “driving cars/trucks/heavy equipment” could be regarded as similar activities that share the same PAR value and consume the same amount of unit energy. To help with the subsequent survey, depending on the specific activities and loads carried by a worker, the daily activities listed can be transferred into construction activities. The comparisons are shown in Table 6.2.
Table 6.2 Daily Activity and Construction Activity Relationship Energy Cost Main Daily Activities Related Construction Activities PAR Driving cars/trucks/heavy Driving car to/from work 2.0 equipment Standing, carrying light loads Standing, carrying light (installation, insertion, welding, loads (Waiting on tables, 2.2 concrete pouring, lifting with arranging merchandise) machine/equipment) Walking at varying paces Walking at varying paces without a 3.2 without a load load Collecting Carrying heavy loads, heavy load 4.4 water/wood/materials work Non-mechanized domestic Non-mechanized domestic 2.3 chores (sweeping, washing (sweeping, washing, cleaning, final clothes and dishes by hand) finishing) Sitting (office work, selling Sitting (office work, discussion 1.5 products, tending shop) meeting, task distribution)
In order to collect data on construction worker physical activities while performing different construction tasks, a survey was conducted to gather information from on-site professionals. The survey was separately distributed in the US and China. The US data 30
is the focus of this research; the Chinese data will be used for comparisons in future
research.
Besides questions pertaining to participant demographics and perspectives on
construction industrialization, the survey required a large amount of data input by the
participants in a spreadsheet. Assuming a worker or a working crew is continuously
performing work to construct a design element for 8 hours, the participants were asked
to distribute the 8 hours across the physical construction activities listed in Table 6.2.
For example, if we would like to know how workers distribute their time on different
activities while performing the task of “drilling of hole for piles”, we assume the
workers or crews are doing the same task for the entire day (8 hours). During this 8 hours, maybe 2 hours were spent on walking at varying paces without a load, 3 hours were spent driving equipment, and 3 hours were spent carrying heavy loads. Then we would know how the workers distribute their time while doing this construction task.
On-site professionals who took part in the survey would need to distribute the 8-hour time to the six working activities based on his/her experience and knowledge of best practices. This data entry is required for more than 900 construction tasks. It was impossible for one person to be familiar with all of the construction processes and to finish the complete set of the survey. Participants were asked to only fill in the data for the construction processes in which they were familiar.
Survey distribution and data collection first started in December 2016 and the entire duration lasted for nearly half a year. The survey was distributed mainly through emails and face-to-face interviews to help the participants understand the survey clearly. More than 100 contractors were contacted and nearly 50 professional construction engineers 31
from China and the US took part in this research. Given the large amount of data entry
required, and the targeted audience (construction field staff with experience in
commercial building construction), a convenience sample was used for the survey.
Participants were identified and selected for participation through a site interview and email contact list. Several of the construction projects contracted with different construction companies were visited and interviewed, including Andersen
Construction, Hoffman Construction Co, and Fortis Construction, etc. The on-site project managers, construction engineers, and superintendents were interviewed, and asked to distribute the survey to their sub-contractor professionals. The researchers ultimately received at least three complete data sets for all of the design elements and construction activities from China and also three complete data sets from the US.
6.5 RESULTS AND ANALYSIS
A total of 15 people from the US completed the demographic questions and basic perspective question on construction industrialization included in the questionnaire,
while more than 20 people filled out the data entry in the spreadsheet. Most of the
participants (15) completed both the demographic questions and parts of the
spreadsheet.
From all of the participants who completed the demographic questions collected within
the US (15), 53% of the participants work for general contracting firms and 40% for
sub-contracting firms. To verify that the participants were experienced on-site engineers, a survey question also solicited data of the participants’ work experience in the construction industry. Of the 15 respondents to the question, 40% have more than
20 years of work experience in the construction industry, and 93% have more than 5 32
years of experience working in the construction working field (see Figure 6.2). These
statistics reveal that the data collected was from professional construction engineers
with a substantial amount of work experience in the industry, which ensures the
credibility of the data to be analyzed.
HOW MANY YEARS OF EXPERIENCE DO YOU HAVE IN THE CONSTRUCTION INDUSTRY? Less than 2 years 13% 2 ~ 5 years More than 20 7% years 40%
6 ~ 10 years 11 ~ 20 years 33% 7% Figure 6.2 Work Experience of Survey Participants (n = 15)
For the formal survey, the spreadsheet was designed as the construction activity related to each of the construction tasks and design elements. The survey participants were asked to enter values for each of the six activities, and assign 8-hours of working to each of the construction tasks. In this way, each of the construction tasks was correlated with the applicable activities. The PAR required for each construction task could then be calculated using the energy cost PAR values shown in Table 2 above. Using the method for calculating the expected value, the formula to calculate the construction task PAR is as shown in Equation 6.2:
= (6.2) 𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴 1∗𝐻𝐻𝐻𝐻𝐻𝐻𝐻𝐻𝐻𝐻 𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎 1+⋯+𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴 6∗𝐻𝐻𝐻𝐻𝐻𝐻𝐻𝐻𝐻𝐻 𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎 6 𝐸𝐸𝐸𝐸𝐸𝐸where:𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸 𝑃𝑃𝑃𝑃𝑃𝑃 𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇 ℎ𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜 𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎 33
Activity n = Energy expenditure of the six construction worker activities (shown in Table 6.2) Hours assigned n = Number of hours assigned to each activity within the 8 hours of work on the construction task, from the survey participants’ input
Each of the data sets collected contains more than 900 construction tasks, each of which contains six values of activity hours assigned to the different physical worker activities.
For three different data sets, the median value of the three activity hours received because different data set contain slightly different numbers and the data has outliers; in this case, the median value is more accurate than the mean value of the data.
Table 6.3 Top 5 Construction Tasks with Highest/Lowest PAR value Five Constr. Task with PAR Five Constr. Task with PAR Highest PAR value value Lowest PAR value value Transport of MEP Hoisting of Brick Panel 3.95 equipment to location of 2.059 Sections from Crane installation Lifting and Lowering of Fabrication of Conduit Beam Sections from 3.87 2.188 Sections Crane Making Field Connection 3.8 Excavation work 2.203 Accepting, guiding, Making Connection With carrying of deck section 3.76 2.207 Structural frame to the point of placement Loading Beam Sections 3.7 Pouring Concrete 2.208 From Cranes
After sorting the collected data, the Physical Activity Ratio (PAR) for the construction tasks was analyzed based on the respondents’ personal opinions and on-site experience.
The PAR value for different construction tasks varied from as low as 2.06 (transporting of MEP to location of installation) to as high as 3.95 (hoisting of panel sections from cranes), with an average of 2.66. Among all of the construction tasks, the most tasks that consume the most energy are those that require heavy work without the assistance 34
of machines and equipment, such as hoisting heavy sections from cranes (PAR = 3.95),
lifting and lowering heavy sections from cranes (PAR = 3.87), and making field
connections of prefabricated brick panel curtain walls (PAR = 3.80). Take the task
“hoisting heavy sections from cranes” as an example. Even though the task identified
that the laborers are using help from cranes, the construction workers still need to hold,
push, and locate the panel sections to the right spots. The workers are still performing
at a high strength level of activity for this task. If there are ways to use equipment
instead of human labor to conduct the work, the energy expenditure for on-site labor
decreases significantly.
According to the PAR survey spreadsheet and construction workers’ possible activities
listed in Table 6.2, the activity that has the lowest PAR value is sitting while performing
office work and attending meetings, which would not be the main activity for on-site construction tasks. The activity that has the second lowest PAR value of 2.0 is driving, including driving cars and trucks and operating heavy equipment, which is the main activity that construction workers would do for the entire time while performing some of the construction tasks. For example, transporting equipment to the location of installation has a low PAR value (2.06), which makes sense based on the energy expenditure for the truck or equipment driver.
Comparing among different construction tasks and design elements, the top five design elements that consume most human energy are prefabricated brick panel curtain walls
(PAR value = 3.47), glass fiber-reinforced concrete curtain wall (3.40), stone cladding on steel trusses/prefabricated stone curtain walls (3.37), fabricated stone honeycomb curtain wall (3.35), and stone-honeycomb panels (3.20). These energy-consuming 35
design elements have a commonality in that most of them are human activities
performed “from a crane”. If these activities could be performed with the assistant of
machines, there would be less need for humans to consume large amounts of energy,
which would also prevent the risks of heavy work injuries and potential safety issues.
When considering the basal metabolic rate (BMR) value for male and female construction workers, the unit energy expenditure for all construction tasks and design elements can be calculated. The unit energy expenditure while operating different construction tasks varies from 153.6 calories/hour (0.64 Megajoules/hour) to 294.6 calories/hour (1.23 Megajoules/hour) for male workers, and from 121.4 calories/hour
(0.51 Megajoules/hour) to 232.7 calories/hour (0.97 Megajoules/hour) for female workers. The only difference between male workers and female workers is the different
BMR values. According to Bureau of Labor Statistics in 2014, women working in construction numbered only 1.2 percent of the entire U.S. workforce in 2013, much lower than the workforce average. It indicates that construction tasks done by human laborers would cost more human energy than other industries, which shows the replacement of human workers with machines is necessary in terms of reducing energy expenditure.
6.6 RESEARCH LIMITATIONS Limitations in the applicability of the research findings to all construction work exist.
Since the PAR survey was mostly based on the on-site professional’s personal opinions and experiences, the results of the data collected are subjective, which could result in biased opinions and input by the participants. The effects of the subjective input could be reduced by utilizing input from a larger number of participants. 36
Another limitation relates to the difficulties of collecting needed data. The survey
requires a large number of values to be input in the spreadsheet, which is not easy to collect using an unpaid survey. Participant fatigue may skew the values input. The small number of participants who provided data is also an issue that may affect the accuracy of the survey results. Three data sets for this survey may demonstrate a basic idea of how much energy is required for construction workers while operating different
construction tasks. However, to improve the accuracy of the results for detail
calculations, a larger amount of data input is needed.
6.7 CONCLUSIONS AND RECOMMENDATIONS
The primary research goal for this study was to calculate the energy expenditure for on-site construction workers while performing different construction tasks. To reach this objective, the researchers introduce the concepts of basal/resting metabolic rate
(BMR/RMR) and physical activity ratio (PAR) for construction activities. In order to identify the energy expenditure differences among different construction tasks and design elements, a survey was conducted to collect opinions from experienced, on-site
construction engineer regarding how construction workers distributed their times on
different activities while performing different construction tasks. The results reveal that
the unit energy expenditure while performing different construction tasks varies from
153.6 calories/hour (0.64 Megajoules/hour) to 294.6 calories/hour (1.23
Megajoules/hour) for male workers, and from 121.4 calories/hour (0.51
Megajoules/hour) to 232.7 calories/hour (0.97 Megajoules/hour) for female workers.
By applying the quantities of different construction tasks needed to construct a project
(e.g., linear foot of structural steel columns or cubic yards of cast in place concrete 37
square columns), the total on-site human energy expenditure for a construction project can be calculated. This value of energy can then be combined with the amount of energy expended by machines and equipment to determine the extent to which the project involves industrialization, pre-fabrication, automation, and artisanship.
For further study, more statistical input is needed to improve the accuracy of the PAR calculation. This additional input is a continuous improvement process for this quantification model; with more people and projects’ participating in data collection for the model, the more accurate the model will be. Engineers and owners of future construction projects could use the model to develop a more accurate prediction and estimation of industrialization for their projects. In addition, with the development of new techniques and new technologies, some of construction tasks and design elements may be out of date and could be replaced with the latest construction techniques that consume less energy. This step is just the first part of the development of the construction process quantification model (CPQM); further phases of the study will include the calculation models of on-site energy expenditure for machines/equipment, and off-site construction processes.
6.8 REFERENCES
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CPWR. 2013. "Age of Construction Workers by Union Status, Hispanic Ethnicity,
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http://www.fao.org/docrep/007/y5686e/y5686e00.htm. 40
7. ENERGY EXPENDED BY MACHINES PERFORMING ON-SITE
CONSTRUCTION TASKS
Note: This Chapter provides supplementary content for the first manuscript.
Manuscript #1 showed the feasibility of using energy expenditure to measure construction tasks, and determined the energy expended by construction workers for on-site construction tasks (the artisanship part in the CPQM). However, Manuscript #1 does not mention the energy expended by machines during on-site construction tasks.
This Chapter covers the evaluation of energy expended by on-site construction equipment.
7.1 INTRODUCTION
On construction sites, multiple machines and pieces of equipment are utilized to fulfill many construction functions. Since the first industrial revolution, machines and tools have been introduced in multiple industries to replace human workers (Wikipedia,
2019). Before the use of machines, the construction industry was largely dependent on human laborers to cut and move materials, and lift up stones and heavy masonry.
Multiple tools were created to ease laborers’ workloads. For example, tackle used for lifting was developed and considered as an early form of cranes (Clarke & Engelbach,
2014). The utilization of machines and equipment on construction sites largely decreased the risks of construction workers from experiencing overloaded work tasks
(Liu & Gambatese, 2018; Chapter 6.7), and safety and long-term health issues.
Currently, there are various equipment companies providing equipment services and guidance for construction projects. There are thousands of different types of equipment 41
being utilized on construction sites, and in transportation work zones, the heavy civil field, and off-site factories. The way to correctly identify the equipment needed is extremely important in the process of creating the CPQM model.
7.2 METHODOLOGY
The use of on-site construction equipment varies on different projects due to the complexity of the project, volume of work, building purpose, main structures, and other characteristics of the projects. Since there will always be new techniques and technologies used in the construction industry, including all construction equipment in this research study seems to be an impossible mission.
The researcher tried to include as many types of on-site construction equipment as possible, so multiple site observations and photo taking were needed for equipment identification. Based on the site interviews and observations, related brands and models of the equipment were recorded for subsequent identification of the equipment via an
Internet search. It is assumed that equipment specifications are listed on the official websites of different equipment and service providers.
7.3 FINDINGS
Accordingly to the data posted by statista.com in 2018, the largest construction machinery manufacturer is Caterpillar (a U.S. company), with annual sales of $26.64 billion US dollars in 2017. Komatsu and Hitachi (both Japanese companies) were in the second and third place with annual sales of $19.24 billion and $8.3 billion US dollars, respectively (Statista, 2018).Based on its most recent data posted online
(Caterpillar, 2019), with more than 90 years of history, Caterpillar is now hiring 42
104,000 full-time employees, and made year-end 2018 sales and revenues of $54.7 billion US dollars, including its production lines of construction equipment, mining equipment, diesel and natural gas engines, industrial gas turbines, and diesel-electric locomotives. The top three companies mentioned shared more than half of the global construction heavy equipment market. And, as a US company, Caterpillar equipment is present on most construction sites in the United States. The researcher’s observations on some of the construction sites proved that CAT (Caterpillar) equipment can be seen nearly on every construction site.
Based on the site observations and recordings, the researcher was able to identify different types of equipment that are commonly used on most of the construction sites.
The scope of the identification extended from the foundation process to the final mechanical, electrical, plumping system installation process. The researcher also referred to the construction task list mentioned in Manuscript #1 (Chapter 6.3), and analyzed the equipment required to complete each of the construction tasks. The identified on-site machines and equipment are: pile rigs, excavators, dozers, wheel loaders, cranes, lifts, concrete pumps, generators, and air compressors. For this research study, the research only focuses on the construction process, including the processes used to construct foundations and footings, structural frame, low slope roofing, exterior enclosure, interior construction, and the MEP systems. As a result, the equipment for pre-construction (e.g., heavy civil earthmoving equipment) and post-construction (e.g., lawn mower) are not included in the list, and will not be considered in this research study. 43
For each type of equipment, there are multiple models associated with each of the types.
Different models may typically have different horsepower, which is associated with the hourly energy expenditure. For example, cranes can be categorized into three types: crawler cranes, truck mounted cranes, and tower cranes. For each type of crane, there are different models provided by multiple companies. Collecting all of the models from every company for each piece of equipment is not feasible because, on one hand, it requires a large amount of work to finish the process and on the other hand, old models are salvaged and new models are created regularly. Even with a high level of care, there still could be a chance that some models will be missed. Information on all equipment models could never be up to date. Therefore, the researcher simplified the process and used small-medium-large scale (or light-medium-heavy duty, or similar scale) to categorize different types of on-site equipment.
The standard used to define small-medium-large scale equipment is basically dependent on the scales defined by Caterpillar for its own products. Take CAT dozers as an example. CAT categorized its dozers into four levels: small, medium, large, and waste handling (CAT, 2017). For each of the levels, several models fit within the level.
The researcher calculated the average horsepower of all equipment within the same level to generate a value that could be used for estimating energy expenditure. For those pieces of equipment that CAT does not produce, the researcher searched the most relevant brands and categorized the equipment based on their horsepower or wattage ratings. 44
As a result, the researcher was able to identify a list of all nine categories of on-site
equipment as mentioned previously in this chapter. The list contains 14 different types
of equipment with a total of 41 different models (representing different horsepower
ratings for the equipment). Detailed results of the categories, types, models, and related
wattages are listed in Table 7.1. To clarify validation of the data, all of the data collected was based on a web search in the year 2017.
Table 7.1 List of On-site Construction Equipment
Average Categ. Types Models (Level descriptions) Wattage (kW) Small (torque is around 60–100 kN m, engine power 108 kW, drilling diameter 0.5– 108 1.2 m, drilling depth 40 m, total quality 40 t) Pile drilling Medium (torque is around 120–180 kN m, engine (Wikipedia, power 125–200kW, drilling diameter 0.8–1.8m, 152.5 Pile 2018) drilling depth 60m, total quality 42–65t) Rigs Large (torque is around 240 kN m, engine power 300 kW, drilling diameter 1–2.5 m, 300 drilling depth 80 m, total quality 100 t) Pile Driving On-shore (Single acting) 114.4 (Vulcanham On-shore (Double acting) 169 mer, 2017) Vibratory hammer 404.25 Minis (19 CAT models) 23.6 Small (8 CAT models) 72.2 Excavators Medium (12 CAT models) 138.8 (CAT, 2017) Large (8 CAT models) 293.9 Frontless (8 CAT models) 247.1 Small (4 CAT models) 70.8 Dozers Medium (5 CAT models) 142.6 (CAT, 2017) Large (4 CAT models) 409.8 Waste (5 CAT models) 210.4 Compact (8 CAT models) 60.9 Wheel Loaders Small (8 CAT models) 120.0 (CAT, 2017) Medium (17 CAT models) 230.6 Large (13 CAT models) 540.2
45
Table 7.1 List of on-site Construction Equipment (Continued) Small (Nominal capacity < 100 US tons) 138 Crawler Medium (100
7.4 RESULTS AND CONCLUSIONS
Based on the collected data, the researcher was able to identify the most common types of on-site construction equipment. The equipment listed in Table 7.1 is assumed to cover all construction on-site tasks for which large equipment is typically used. One or multiple pieces of equipment may be used for a single task. Energy expended by each construction task could be evaluated by multiplying the combination of equipment wattages by the number of hours that the equipment is used. In this research study, the 46
usage of on-site equipment is based on the work hours required for each task. It is
assumed that if the worker is working on a specific construction task, related equipment
is also running via a human operator or on its own. The limitation of this assumption is
that for some tasks, machines are not running all of the time. Take the task “hoisting of
column section using crane” as an example. A crane is one of the pieces of equipment
that is utilized. Hoisting a component from one place to another requires preparation
time for the workers, and during this time, the cranes do not do the lifting work, which
may not require the designated horsepower. In this case, it is hard to measure the actual wattage for the idle condition of the cranes, which might result in inaccuracies in the estimation of the energy expended by the equipment. There are many variable factors influencing actual energy expenditure of on-site equipment. Using the designated wattage or horsepower is the most feasible way for energy estimating.
In the CPQM model, the end-user indicates the models of the different types of on-site equipment used on the project. Due to the variety of different construction projects, utilization of on-site equipment varies irregularly. The researcher decided to leave the option of equipment model selection up to the end users. The CPQM model will show the list of on-site equipment commonly used for each of the tasks, and end users will then be able to choose which equipment and model, and how many, will be used based on the actual situations on the targeted project. In the CPQM model creation, the energy expended by the identified on-site equipment will be categorized in the automation part
(on-site energy expended by machines). 47
7.5 REFERENCES
CAT, 2017. CAT Equipment Excavators. [Online] Available at:
https://www.cat.com/en_US/products/new/equipment/excavators.html
[Accessed 17 March 2017].
CAT, 2017. CAT Equipment Wheel Loaders. [Online] Available at:
https://www.cat.com/en_US/products/new/equipment/wheel-loaders.html
[Accessed 17 March 2017].
CAT, 2017. CAT Equipment-Dozers. [Online] Available at:
https://www.cat.com/en_US/products/new/equipment/dozers.html[Accessed
10 April 2019].
Caterpillar, 2019. Caterpillar Fact Sheet | Based on year-end 2018 data. [Online]
Available at: http://s7d2.scene7.com/is/content/Caterpillar/CM20190320-
32743-09077 [Accessed 10 April 2019].
Clarke, S. & Engelbach, R., 2014. Ancient Egyptian Construction and Architecture.
Dover edition ed. New York: Dover Publications, Inc.
CROWN, 2017. Forklifts-Superior Perfromance, Award-winning Design. [Online]
Available at: https://www.crown.com/en-us/forklifts.html[Accessed 20 March
2017].
Genie, 2017. Genie lifts A TEREX BRAND -Aerial Lifts. [Online] Available at:
https://www.genielift.com/ [Accessed 18 March 2017]. 48
Liu, D. & Gambatese, J., 2018. Energy Expenditure by Construction Workers for
On-site Activities. New Orleans, ASCE, pp. 533-542.
Statista, 2018. World's largest construction machinery manufacturers - sales 2017.
[Online] Available at: https://www.statista.com/statistics/280343/leading-
construction-machinery-manufacturers-worldwide-based-on-sales/ [Accessed
10 April 2019].
Sullair, 2017. SULLAIR A Hitachi Group Company - Construction Compressors.
[Online] Available at：
http://www.sullair.com/australia/aircompressors/industrial/construction-
compressors [Accessed 20 March 2017].
Terex, 2017. TEREX Cranes products. [Online] Available at:
https://www.terex.com/cranes/en [Accessed 13 March 2017].
Vulcanhammer, 2017. Resources Specifications. [Online] Available at:
https://vulcanhammer.com/resources/specifications [Accessed 13 March
2017].
Wikipedia, 2018. Pile driver. [Online] Available at:
https://en.wikipedia.org/wiki/Pile_driver [Accessed 11 April 2019].
Wikipedia, 2019. Industrial Revolution. [Online] Available at:
https://en.wikipedia.org/wiki/Industrial_Revolution [Accessed 10 April 2019]. 49
8. MANUSCRIPT #2
CONSTRUCTION WORKER AND EQUIPMENT ENERGY EXPENDITURE
FOR OFF-SITE PRECAST CONCRETE
Ding Liu, John Gambatese, and Wenyuan Song
PCI journal Precast/Prestressed Concrete Inst, 200 W Adams St, #2100, Chicago, USA, IL, 60606 Pending submission and under review
50
8.1 ABSTRACT
The concept of construction industrialization was first raised in the 1960s. Previous studies define construction industrialization as moving on-site construction work to off-
site factories, largely depending on the assistance of automated machines and
equipment. In some developing countries, industrialization of construction projects is
highly recommended to minimize construction waste and pollution. However, no
formal method has been developed to calculate the industrialization rate of construction
projects. The overall goal of the study is to develop such a method. To do so, the
researchers utilize energy expenditure as a basis for assessing industrialization rate.
Previous research has investigated the energy expended by construction workers when
performing on-site activities. To determine the differences between on-site and off-site
processes in terms of energy expenditure, the present study focuses on off-site construction processes and the related energy expenditure. The findings of the study provide input to create a quantification model of the construction process. To collect accurate and persuasive data, survey and site-observation research methods were utilized. The researchers chose the overall concrete construction process as the main focus for the study, and conducted a survey of precast concrete plant personnel and observed precast concrete plant operations in the Pacific Northwest. Based on the data analysis, the physical activity level of off-site precast concrete processes was quantified to be 2.51, which is located in comfort zone for human work. In addition, with newly defined levels of automation (LOA), the researchers developed correlations between the LOAs and the ratios of power to plant size, production, and expenditure,
respectively. The findings from the study provide foundational knowledge needed to 51
develop a method to quantify the industrialization rate on construction projects. With
such a method available, project stakeholders can make decisions based on the extent
of construction industrialization and the benefits that it provides.
8.2 INTRODUCTION
Cost, schedule, quality, and safety are primary performance criteria used for
construction project evaluation and, as a result, given significant attention in the
industry. Industrialized or modularized construction can be one of the solutions to
improve construction performance and reduce construction waste. The rate of
construction industrialization is considered to be an indicator for measuring the amount
of modularization of a project. However, formal methods to calculate this rate are not
available for use in practice.
The Construction Process Quantification Model (CPQM), introduced by Liu and
Gambatese (2018), provides a quantification method to evaluate the rate of construction
industrialization, together with the rates of artisanship, automation, and pre-fabrication on a project. For the development of the model, the researchers converted all of the units of construction associated with a typical commercial building to energy expenditure, and analyzed worker energy expenditure while performing the requisite construction tasks. This prior study focused on on-site activities. To further develop the
CPQM, additional data collection and analysis are needed for the off-site components within the building construction process (i.e., pre-fabrication and industrialization).
While the theoretical backbone of the present research was introduced by Liu and
Gambatese (2018), the research content and focus is different than the previous study. 52
The previous study focused on computing the energy expenditure for on-site construction tasks, while the research presented in the present paper addresses the energy expended by off-site tasks, and specifically focuses on off-site construction processes.
The research presented in this paper analyzes off-site construction processes and makes comparisons between energy expenditure of on-site construction tasks and off-site construction tasks. To achieve this goal, several research questions need to be answered.
Initially, what construction components could be produced off-site in a factory to replace on-site construction processes? Then, are there any differences in worker energy expenditure between the on-site and off-site construction processes? And finally, will energy expenditure for a project be reduced if on-site processes are replaced with off-site processes in terms of both workers and machines/equipment?
For the purposes of the study, the researchers selected the construction of concrete elements within buildings as the target design element to investigate. Therefore, the study entailed investigation of construction processes at off-site precast concrete plants.
With the potential differences in level of automation within different off-site concrete plants, it is presumed that observed precast concrete plants have different productivity and working efficiency. The level of automation and its relationship to size of plant, number of employees, and productivity, is also a focus of the research that will be discussed in this paper. 53
8.3 BACKGROUND AND LITERATURE REVIEW
For clarification, it is necessary to begin by mentioning the construction tasks and categories of construction processes established from previous research. Accordingly, by location of the construction process, all construction tasks can be divided into on- site and off-site tasks. In addition, with respect to performing the operations, construction tasks can either be conducted by laborers or by machines/equipment. With those two axes, the researchers organized all construction tasks into four construction process categories, shown in Figure 8.1: artisanship (on-site by laborers), pre- fabrication (off-site by laborers), automation (on-site by equipment), and industrialization (off-site by equipment). The previous study also defined each of the four categories and created a method to calculate the energy expenditure of all construction tasks for a project that goes into each of the categories.
Figure 8.1 Categories of Construction Tasks Consistent with the previous research (Liu and Gambatese 2018), in the present study the researchers used energy expenditure to calculate the percentages in each of the categories. All work performed by laborers and machinery consumes energy. Calories 54
and KWH were the units of energy used in the present study for laborers and machinery,
respectively. While other measures could be used, such as cost, energy was believed to
be the best choice to evaluate how different construction tasks were distributed in
different categories. There are many methods that could be used to measure energy
expended by laborers and equipment. The detailed energy evaluation method utilized
in the study is discussed in the following sections.
The use of off-site construction components or modules on projects is intended to
reduce schedule and cost, improve quality, and enhance worker health and safety.
Research shows that when parallel on-site and off-site processes are used to produce
buildings, an overall reduction in construction time of 50-60% results (Boyd, et al.
2013). Additionally, research studies have demonstrated that an average of 52% waste
reduction and 70% timber formwork reduction can be achieved while adopting precast
construction methods. In terms of productivity, off-site concrete construction reduces the chance of operational problems and site disruption (Blismas, et al. 2006) which, in
turn, increases the operation speed of the same concrete modules. Thus, off-site construction processes have been shown to provide significant advantages in terms of reduced waste, less pollution, improved safety, and shorter project schedules. However, as described below, concerns exist regarding implementing precast concrete processes.
A recent research study identified driving factors of construction industrialization (CI), and mentioned concerns about performing CI. First, a lack of professional laborers is one of the obstacles to off-site construction identified. Performing on-site construction operations and on-site construction module assembly are two different processes; to 55
implement off-site construction together with on-site construction module assembly,
more workers are needed who are trained to perform the task. Significant concerns with
the supply chain were also expressed by the research participants for increased off-site construction. The number of precast concrete plants and their capacity might become a new bottleneck initially as construction industrialization is developing. Other driving factors, including management methods, the level of management available, structural stability, and the reduction of construction schedules and costs, were also considered as concerns for owners and contractors when deciding whether to implement industrialized processes on their projects.
8.3.1 Extent of Off-site Construction
The issue of what construction component(s) could be produced off-site has been discussed within the industry for years. When considering which on-site processes could be replaced by off-site activities, previous researchers have listed definitions of
four off-site levels of construction generally associated with the degree of off-site work
undertaken on the product. As shown in Table 8.1, off-site levels of construction were
categorized starting from Level 1 - Component manufacture and sub-assembly, to
Level 4 - Whole building. Level 1 contains the items that are always produced in a factory and never considered for on-site production, such as steel components (beams, girders, and columns, etc.), windows, and doors. Level 4 off-site construction is
feasible for smaller projects, such as small modular apartments or houses, but is less
feasible or even impossible for larger and more complex projects. Larger buildings
prefabricated as a whole off-site will have all kinds of obstacles to industrialization like
transportation, structural stability, and foundation settlement. 56
For the present study, the off-site processes focus on Level 2 - Non-volumetric pre-
assembly, which could be used to replace some on-site processes. Concrete structural elements, such as concrete columns, beams, and girders, are highlighted as the research focus of the study.
Table 8.1 Levels of Off-site construction for buildings
Level Category Definition Component Items always made in a factory and never
1 manufacture & considered for on-site production sub-assembly Non-volumetric Pre-assembled units which do not enclose 2 pre-assembly usable space Pre-assembled units which enclose usable Volumetric pre- space and are typically fully factory finished 3 assembly internally, but do not form the building’s level of offsite work offsite of level structure ng Pre-assembled volumetric units which also
creasi 4 Whole buildings form the actual structure and fabric of the
In building Source: Adapted from Gibb (1999)
Up to the date of drafting this paper, according to the National Precast Concrete
Association (NPCA), there are a total of 362 NPCA-certified concrete plants in the
United States and Canada. The Precast/Pre-stressed Concrete Institute (PCI) also lists
273 certified plants on its website. Compared to the total amount spent on all
construction projects in the US and Canada, estimated to be more than $1.3 trillion in
2018, the number of precast concrete plants is quite small.
8.3.2 Levels of Automation (LOAs)
Levels of automation (LOAs) are used to analyze the extent to which a task is automated and to specify the degree of automation of the task. Different LOAs have been established and various theories have been developed to perform the evaluation 57
in different circumstances (Kircher, et al. 2014, Frohm, et al. 2008). The utilization of
intermediate LOAs may provide an approach to human-centered automation. LOAs have been developed that are generic to all industries. For example, Sheridan and
Verplanck (1978) developed a LOAs taxonomy associated with the use of computers which is widely used and considered as the original taxonomy of automation. The taxonomy incorporates ten levels of automation as follows:
Level 1: human does the whole job up to the point of turning it over to the computer to implement; Level 2: computer helps by determining the options; Level 3: computer helps to determine options and suggests one, which human need not follow; Level 4: computer selects action and human may or may not do it; Level 5: computer selects action and implements it if human approves; Level 6: computer selects action, informs human in plenty of time to stop it; Level 7: computer does whole job and necessarily tells human what it did; Level 8: computer does whole job and tells human what it did only if human explicitly asks; Level 9: computer does whole job and decides what the human should be told; and Level 10: computer does the whole job if it decides it should be done, and if so, tells human, if it decides that the human should be told.
However, common LOAs tailored to the construction industry have not been
established. The industry lacks construction-centric LOAs to evaluate whether a construction task is human-centered or machine-centered, and to measure the relationship between task performance and its related level of automation. Therefore, to better adapt the levels of automation for the field of construction, and specifically for off-site precast concrete construction, further refinement of the LOAs for construction based on existing theories is needed. 58
8.3.3 Energy Measurement
To analyze energy expended by on-site construction laborers, Liu and Gambatese
(2018) introduced two concepts to their study, Basal Metabolic Rate (BMR) and
Physical Activity Ratio (PAR). BMR and PAR were used to measure energy
expenditure of activities based on the physical intensity of the construction tasks. BMR
is the number of calories required to keep a human body functioning at rest, and its
value is related to a person’s weight, height, age, and gender. PAR represents a person’s
physical activity intensity level, and is the ratio of energy expended in a particular
activity to the person’s BMR (Kuriyan, et al. 2006). Accordingly, daily energy
expenditure can be calculated by multiplying a person’s BMR (with unit of calories/day)
by the PAR for the activities being performed. For example, according to the previous
study (Liu and Gambatese 2018), among all on-site construction tasks, heavy “hoisting of brick panel sections from crane” consumes the most laborer energy on a project with a PAR value of 3.95. This PAR value amounts to an expenditure rate of 294.6
calories/hour (1.23 Megajoules/hour) for average American male construction workers.
The task “Transport of MEP equipment to location of installation,” with a PAR value
of 2.059, requires the least amount of energy for construction workers; the main laborer
activity for this task is driving the vehicle.
As mentioned above, the researchers chose to focus the present study on activities
associated with off-site precast concrete construction. Precast concrete components
manufactured in off-site precast concrete plants can be categorized into two types:
precast reinforced concrete elements and precast pre-stressed concrete elements. Based
on the mixing methods, concrete can be described as either dry-mixed concrete or wet- 59
mixed concrete, which require different work processes. The precast concreting process
for both mixing methods entails the procedures listed in Table 8.2. Both methods
involve placing concrete by pneumatically projecting the material from a hose. The
difference between the methods is when water is added to the material. In a wet-mixed application, all materials are mixed together before being pumped through a hose and pneumatically projected. Alternatively, in a dry-mixed application, all dry materials are mixed together, conveyed pneumatically through a hose and then, at the nozzle via a water ring, water is injected evenly throughout the mix as it is being projected. All precast concrete components and customized modules can be completed with these two mixing methods. According to site observations by the researchers, most precast concrete plants use dry-mixed concrete for standard modules, such as typical concrete manholes and pipe. Wet-mixed concrete is mainly used on customized modules such as concrete walls, bridge modules, and railroad components.
Table 8.2 Precast Concrete Construction Tasks (wet and dry)
Normal Casting Process for Dry Casting Process (Extrusion Precast Reinforced and Pre- Method) for Precast Hollow Core stressed Concrete Elements Elements (Wet mixed) 1 Assembly of mold Base mold cleaning and preparation 2 Mold cleaning and preparation Pre-stressing strand hauling and tensioning 3 Fixing of rebar/cast in items/pre- Concreting stressed strands 4 Final inspection before casting Curing 5 Concreting Detensioning of strands 6 Curing Final inspection/ Transfer to storage yard 7 Demolding 8 Final inspection/ Transfer to storage yard
60
8.4 METHODOLOGY
In addition to a literature review, to obtain a better idea of how precast concrete workers perform off-site activities in a factory, the researchers utilized a combination of both surveys and site observations to collect quantitative and qualitative data. Since the on- site construction process chosen to be replaced by off-site activities for the study is the concrete work, the targeted observation sites and locations of the survey participants were off-site concrete plants. Additionally, the researchers utilized the same method of evaluating a laborer’s energy expenditure as that used in the previous study by Liu and
Gambatese (2018).
8.4.1 Research Model
The research plan developed for the study followed the data requirements necessary for calculating the amount of construction industrialization. The model used for quantifying construction industrialization is shown in Figure 8.2, which provides an overview of the research process and illustrates the data required for the quantification model. As shown in the figure, two process categories of off-site construction tasks are included in this research: pre-fabrication and industrialization. For off-site precast concrete production, three major data inputs are needed in order to measure the energy expended by off-site construction tasks: productivity of concrete plant, power consumption by equipment, and worker time distribution on each construction activity.
The research plan was designed to collect the data through surveys of off-site precast
concrete plant personnel and site observations at concrete plants. Described below are
the processes used to determine the: productivity associated with precast concrete plants, energy expended by plant laborers (utilizing the BMR and PAR values 61
described above), energy expended by concrete plant equipment, and levels of automation in construction. These components are used to calculate the amount of energy expended by off-site precast concrete operations, as illustrated in Figure 8.2, and as a result can be used to quantify the amount of industrialization on a project.
8.4.2 Survey of Concrete Plant Personnel
For the purpose of the survey, precast concrete plants are identified as those controlled environments that produce precast concrete modules for construction purposes, which include the processes of concrete manufacturing, modularizing, curing, and storing.
Potential precast concrete plants to include in the survey were identified via an Internet search. Those plants that are listed as PCI Certified Plants on the PCI website were included in the survey sample. The survey questionnaire was distributed to the presidents, site managers, and site engineers of the concrete plants through email contact and/or during site observations. The contents of the questionnaire included demographic questions, information regarding the physical nature of the precast concrete plant, and the participant’s personal perspectives about the performance of the workers and the plant. With respect to the plant size and operations, the questions solicited information on the daily electric energy expenditure, productivity, number of workers, work-time distribution for different activities, and types of equipment on site.
62
Figure 8.2 CPQM model off-site part 63
With worker hour distribution recorded from the survey, the researchers could calculate the PAR
for each working crew (e.g., rebar crew, carpentry crew, formwork crew, etc.) in the plant using
the defined PAR for basic construction activities. The calculated PAR value can then be used to calculate energy expenditure per worker-hour (using Equation 8-2 cited in the next section) and, as a result, the energy expenditure of producing one cubic yard of concrete can be calculated. With the energy expenditure per cubic yard available, future users only need to provide the quantity of concrete needed for each project to calculate the amount of energy expenditure.
With the productivity of the concrete plant already collected, the number of worker-hours for producing the concrete modules can be calculated (Eq. 8-1). Equation 8.1 is a simple mathematical unit conversion: cy/(cy/hour) = hour. Total energy expenditure of off-site laborers and equipment can then be calculated, and finally categorized into pre-fabrication and industrialization, respectively.
( ) ( ) = (8.1) ( ) 𝑄𝑄𝑄𝑄𝑄𝑄𝑄𝑄𝑄𝑄𝑄𝑄𝑄𝑄𝑄𝑄 𝑜𝑜𝑜𝑜 𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐 𝑛𝑛𝑛𝑛𝑛𝑛𝑛𝑛𝑛𝑛𝑛𝑛 𝑐𝑐𝑐𝑐 𝑐𝑐𝑐𝑐 8.4.3 Energy𝑀𝑀𝑀𝑀𝑀𝑀 Expendedℎ𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜 𝑅𝑅𝑅𝑅 by𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅 Laborersℎ𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜 𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃 𝑜𝑜𝑜𝑜 𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐 𝑚𝑚𝑚𝑚𝑚𝑚𝑚𝑚𝑚𝑚𝑚𝑚𝑚𝑚 ℎ𝑜𝑜𝑜𝑜𝑜𝑜
To compute the energy expended by off-site laborers, the researchers used the same method as that
used for on-site construction tasks calculation (Liu and Gambatese 2018). Since BMR represents
the basic metabolic energy expenditure in one day (24 hrs), daily energy expenditure of a person
can be calculated by multiplying the BMR times the physical activity level of different tasks.
Therefore, the hourly energy expended by laborers while performing different construction tasks is calculated using Equation 8.2:
64
( / ) = . / (8.2) 𝐵𝐵𝐵𝐵𝐵𝐵 𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐 𝑑𝑑𝑑𝑑𝑑𝑑 where:𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸 𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐 24 ℎ𝑟𝑟𝑟𝑟 𝑑𝑑𝑑𝑑𝑑𝑑 ∗ 𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶 𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡 𝑃𝑃𝑃𝑃𝑃𝑃 BMR = 1,889 calories/day for a male construction worker and 1,493 calories/day for female construction workers, based on the average values of height, weight, and age of an American construction worker.
Energy cost PAR is the ratio of the energy expended relative to the BMR. In a research report titled
“Human Energy Requirements,” Tontisirin and Haen (2001) identify the daily physical activities and their PAR values. Based on that report, all daily physical activities can be categorized into three lifestyles: (1) Sedentary or light activity lifestyle, (2) Active or moderately active lifestyle,
and (3) Vigorous or vigorously active lifestyle. To quantify the intensity level of construction
activities for the present study, the researchers selected six of the daily physical activities and
converted them to construction activities identified in the previous study. The selected activities
cover nearly all possible activities that construction workers perform. Take “driving” as an
example daily physical activity. The activity consumes twice (PAR = 2.0) as much energy as a
person’s BMR. In this case, it is assumed that the daily physical activity “driving car to/from work” and the construction activity “driving cars/trucks/heavy equipment” are similar activities that share
the same PAR value and consume the same amount of unit energy. It is similarly assumed that the
daily physical activity “collecting water/wood/materials” expends the same amount of energy as
the construction activity “carrying heavy loads, heavy load work.” Similar connections to
construction activities were made for the other four daily physical activities.
Like the methods used previously for analyzing construction tasks/processes, the researchers
analyzed worker behaviors while performing the two concrete mixing methods for the design
elements. The goal was to determine the physical activity level of each task by measuring how the
workers distribute their time according to the six converted construction activities while
performing each task. Based on the work processes listed in Table 8.2, the precast concrete plant- 65
related job occupations are identified as dry plant workers, wet plant workers, gantry crane
operators, maintenance workers, rebar workers, formwork workers, assembly workers, and other
workers. The identified worker occupations were confirmed to be feasible in the subsequent site
observations and surveys. Worker behaviors for each of the worker occupation types were
recorded to obtain the PAR value for each of the construction tasks for both wet and dry plant
design elements. Detailed calculations for this process are described in the Data Analysis and
Results section below.
8.4.4 Energy Expended by Equipment
Energy expended by the equipment and machines in a concrete plant can be easily measured based on the daily electrical power, natural gas, or other types of fuel consumed. When quantifying the
energy expended, the amount of energy is reflected in the concrete plant’s electrical power bill.
However, there are concerns using this data since the power bill includes all the other possible
electrical power expenses within the plant in addition to the electrical power consumed to create
the precast concrete, such as the electricity needed for office use, space heating and air
conditioning, etc. For the present study, the researchers established a boundary for the energy
expenditure considered for the research to exclude the energy expended by office operations.
Office operations energy expenditure was considered as non-value added energy which should not
be taken into account when assessing the energy expenditure associated with the concreting
process. According to a report published in 2010 (E Source 2010), after analyzing data from the
U.S. Energy Information Administration, a U.S. office building typically spends nearly 29 percent
of its operating expenses on utilities. Thus, on average, the non-office operating energy for a
precast concrete plant can be assumed to be 71% of the total electric bill of the plant. Therefore, 66
for the present study, a value of 71% was used to represent the portion of a plant’s electric bill that is devoted to the plant’s pre-casting operations.
8.4.5 Levels of Automation in Construction
The level of automation within a plant is one of the impact factors that influence the productivity and power consumption of the plant. The researchers desired to identify the level of automation of each plant in order to accurately compare plants based on levels of automation. To do so, the researchers started with the taxonomy of ten levels of automation defined by Sheridan and
Verplanck (1978). In their paper, Sheridan and Verplanck detail the roles that human workers and computers play in data collection, decision making, performance, and notification processes. Using the levels established by Sheridan and Verplanck, the researchers identified differences among the ten different levels of automation through six aspects of automation in order to create a list of five levels of automation that works for the construction field. The six aspects of automation comprised: data collection, decision alternatives (Alt.), decision selection (Sel.), decision approval (App.), performance, and notification. The newly-defined five levels of construction automation focus on the intelligence of automated systems, and categorize all possible construction tasks into different levels. Table 8.3 shows the conversion of Sheridan and Verplanck’s levels of automation to those established for construction for the present study. 67
Table 8.3 Levels of Automation (LOA) Analysis and Conversion to Construction
Data Decision Notific Constr. LOA Description Perform Description Collection Alt. Sel. App. ation LOA Human does the whole job up to the Construction workers do the 1 point of turning it over to the H* H H H H N/A 1 whole job with assistance of computer to implement human-controlled machine Computer helps by determining the Sensors embedded to collect 2 C** C/H H H H N/A 2 options data for human to analyze Computer helps to determine 3 options and suggests an option, C C H H H N/A Intelligent automated system; which human need not follow system assists human Computer selects action and human 3 4 C C C/H H H N/A workers on data analysis and may or may not do it decision-making Computer selects action and 5 C C C H H N/A implements it if human approves Computer selects action, and 6 informs human in plenty of time to C C C C C/H N/A stop it Highly intelligent automated Computer does whole job and Necess system; system makes 7 C C C C C necessarily tells human what it did arily decisions and performs the Computer does whole job and tells 4 work. Provides performance Need 8 human what it did only if human C C C C C report and system warning to to ask explicitly asks human workers when Computer does whole job and C. necessary. 9 decides what the human should be C C C C C decide told to tell Computer does the whole job if it Highly intelligent automated C. decides it should be done, and if so, system; system makes decide 10 tells human, if it decides that the C C C C C 5 decisions and performs the not to human should be told work without notifying tell human workers. *H represent Human-controlled process; **C represents computer-centered process 68
The categorization of the five levels of automation in construction is based on the
intelligence of the automated system. In Level 1 for construction, human workers are
responsible for completing all of the tasks and for control of all processes during
construction operations. This level is considered to be the lowest level of automation,
or the “non-automation” level. In Level 2, data collection is completed by the sensors
and automated equipment, so the workers no longer need an inspector to check the
moisture level, temperature, volume, or other quality control metric all of the time.
Level 3 adds the decision-making or data analysis function to the automated system.
Sensors collect data from the work site and send the information back to the computer, and the computer performs the analysis and decides when, where, and how to make adjustments to optimize the production rate if needed. Under such circumstances, the computer still needs human workers to approve its decision and perform the changes and adjustments.
However, in Level 4, the computer has access and permission to make decisions and perform what it thinks to be right; human workers may receive the operation log or notifications from the automated system to ensure the processes are on the right track.
Level 5 automation would be an extreme level for construction. The automated system in Level 5 has full access to all of the processes including data collection, decision- making, and process performing. The difference between Levels 4 and 5 is that in Level
5, human workers do not necessarily receive notifications from the system. The system chooses necessary information that it thinks human workers should be told. 69
The researcher identified five construction automation levels instead of using the original ten levels. The reason for using five levels is because the boundaries between the original ten levels are not clear in each case based on data collection, decision making, performing, and notification categorization. In the construction industry, the automation levels currently could not attain such an accurate classification. Similarly, the automation levels of vehicles that are widely accepted in the automotive industry is identified to be six automation levels (level 0 to 5) (SAE 2017).
8.5 DATA ANALYSIS AND RESULTS
8.5.1 Demographic Information
Based on the literature review and Internet search, there is not a large number of precast concrete plants within the US. When contacted, only a few of the plants were available and willing to participate in the survey and provide information. As a result, a total of
16 different concrete plants provided input, 10 of which finished the essential parts of the survey, and 6 of the 10 provided input for the entire survey including the PAR analysis and process analysis. The majority of the survey participants (15 of 16) are from the states of Oregon, California, and Washington. Hence, inference of the study is confined within the area of the Pacific coast. Half of the professional engineers who provided feedback have more than 20 years of work experience in the industry, and 88%
(14 of 16) of them have more than 10 years of work experience. Based on the input included in the 10 responses that provided essential information, off-site precast concrete plants typically produce modules such as utility vaults, wall panels, customized railroad products, columns, and architectural pieces. 70
8.5.2 Variable Explanation
From the survey results and site observations, the researchers were able to collect relevant data from the precast concrete industry. To better measure the energy expended by off-site construction workers and equipment, the researchers identified the following variables from the essential sections of the 10 survey responses provided by the precast concrete plants:
• Employee Size – integer value; total number of workers in the precast concrete
plant including gantry crane operator, dry plant workers, wet plant workers,
maintenance workers, rebar workers, and assembly workers
• Plant Size – numerical value; area of the precast concrete plant; unit: acres
• Production – numerical value; volume of concrete that the precast concrete
plant could produce each day; unit: cubic yard (cy)
• Consumption – numerical value; volume of concrete that the precast concrete
plant could consume to produce modules each day; unit: cubic yard (cy)
• Daily Power – numerical value; daily electrical power consumption of the
concrete plant with non-value added energy extracted; unit: kwh;
• Level of Automation – values (i = 1, 2, 3, 4, 5) that indicate the taxonomy of
construction automation from Level 1 to Level 5, based on Table 8.3;
• PAR – numerical value; Physical Activity Ratio; measures the activity intensity
level of off-site construction tasks for both wet-mixed concrete and dry-mixed
concrete methods 71
8.5.3 Energy Expenditure by Workers
In the PAR part of the survey, the participants were asked to enter values for each of
the six construction activities converted from daily activities, and assign 8-hour of
working to each of the construction tasks. For example, assume a worker is performing
task “off-load truck” for continuous eight hours, the survey recorded how much time
will laborers spent on six construction activities based on participants’ best judgment.
In this way, each construction task was correlated with the applicable activities. From
all survey participants who provided full responses, the researchers were able to
analyze the PAR value by calculating the median value of all responses. It is assumed
that using the median value is more accurate for the analysis than the mean value since
the different data sets contain slightly different numbers of responses and the data does
not have an outlier. The researchers used the method for calculating the expected value.
The formula to calculate the construction task PAR is as shown in Equation 3:
( ) ( ) ( ) ( ) =
𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴 1 𝑃𝑃𝑃𝑃𝑃𝑃 ∗ 𝐻𝐻𝐻𝐻𝐻𝐻𝐻𝐻𝐻𝐻 𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎 1 +⋯+ 𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴 6 𝑃𝑃𝑃𝑃𝑃𝑃 ∗ 𝐻𝐻𝐻𝐻𝐻𝐻𝐻𝐻𝐻𝐻 𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎 6 (8-3) 𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸 𝑃𝑃𝑃𝑃𝑃𝑃 𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇 ℎ𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜 𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎 where: Activity n PAR = PAR value of each construction worker activity converted from daily activities Hours assigned n = Number of hours assigned to each activity within the 8 hours of work on the construction task, from the survey participants’ input
As a result, the researchers were able to calculate the physical activity ratio for all of the tasks associated with the wet-mixed method and the dry-mixed method, as listed in
Table 8.4. As shown in the table, the PAR values ranged from 2.08 to 2.66, with a mean value of 2.51. The intensity levels of the tasks are located in a very suitable and 72
relatively low level that do not require human workers to consume much energy for
concrete production and modularization.
Table 8.4 PAR value for off-site precast concrete tasks
Wet-mixed method PAR Dry-mixed method PAR 1 Base mold cleaning and Assembly of mold 2.643 2.638 preparation 2 Mold cleaning and Pre-stressing strand hauling and 2.638 2.55 preparation tensioning 3 Fixing of rebar/cast in 2.55 Concreting 2.621 items/pre-stressed strands 4 Final inspection before 2.485 Curing 2.661 casting 5 Concreting 2.54 Detensioning of strands 2.607 6 Final inspection/ Transfer to Curing 2.5 2.081 storage yard 7 Demolding 2.567 8 Final inspection/ Transfer 2.081 to storage yard 8.5.4 Energy Expenditure by equipment
The researchers aimed to use all of the collected data to initially explore the correlation
among the variables. To do so, it was assumed that the larger the size of the concrete
plant, the more workers it would employ. More workers enable more production, which will result in a higher level of energy expenditure. Based on this hypothesis, the researchers performed a series of analyses to expose the possible relationships between the plant/employee characteristics and energy expenditure.
Due to the small data size (n=5), the researcher was not able to conduct any valuable statistical analyses (Ghasemi and Zahediasl 2012). Since levels of automation are considered as factored variable, the regression model should be considered in discrete variable size. After many attempts, the researcher was not able to collect more data from the precast concrete industry or access the precast concrete plants’ electric power 73
consumption through any other industry. Based on the five data points representing
three different levels of automation, the researcher was able to identify the average
electrical power consumed for each cubic yard of concrete produced for precast concrete plants that fit within Level 1, Level 2, and Level 4 of automation. Figure 8.3 illustrates the relationship between power consumption and level of automation.
Unit production power expended vs. Level of Automation 200
150 136.67
100 48.085
50 20
0 5.496277778 29.16666667 1 2 3 4 5 Power/Production (Kwh/cy) Power/Production Levels of Automation
Figure 8.3 Unit Power consumption vs. Automation Level
Based on the average value, it is estimated that precast concrete plants within
automation Levels 1, 2, and 4 consume 12.748kwh (45.90 Kilojoules), 38.626kwh
(139.05 Kilojoules), and 136.67kwh (492.01 Kilojoules) of electrical energy for each
cubic yard of concrete produced, respectively. For Levels 3 and 5, no concrete plants
were identified in those two levels, so it is not feasible to calculate accurate energy
expenditure predictions for these levels. As previously mentioned, levels of automation
should be considered as a discrete variable, not continuous. With no data located in 74
Levels 3 and 5, the researcher has to leave the power consumption data blank until more input data is collected.
It is assumed that if the levels of automation in construction changed to 10 levels, and with plenty of data collected, the results might be different, and the correlations between the LoAs and unit production energy consumption might be changed.
8.6 LIMITATIONS
There are limitations in the applicability of the research findings to all construction work. Small sample size is always a critical issue when performing construction research. The results concluded from a low number of survey responses might not accurately represent the industry as a whole. If the sample size were larger and could be categorized in all five of the automation levels, the researchers would be able to treat levels of automation as a factor variable. As defined above, Level 5 of construction automation refers to a highly intelligent automated system, which may not exist around the world on this date. Therefore, the researchers are not able to calculate the energy expenditure for Level 3 and Level 5 of automation with real data.
The consequence of small sample size is that the assumptions and hypothesis of the statistical analysis become imprecise and invalid, respectively. Hence, this paper provides a basic methodology of categorizing construction automation levels. With more participants involved, the research would provide stronger evidence of the relationships and a more accurate result.
Another limitation is related to the difficulties in identifying personal perspectives. The
PAR values collected are mainly based on site engineers’ experiences and observers’ 75
site analyses. Participants’ personal opinions are subjective and often biased. In addition, based on the collected durations of the completed surveys, the median duration to complete the survey was approximately 25 minutes. This is a long duration for a survey and may cause participant fatigue, skewing the values input. To improve the accuracy of the results and make the conclusions more convincible, a larger number of responses is needed.
8.7 CONCLUSIONS AND RECOMMENDATIONS
Based on the PAR values presented in Table 8.3, the research revealed that off-site concrete production and modularization consume relatively low levels of energy. For concrete production components of on-site construction tasks, according to previous research, the PAR values for on-site concrete related tasks vary from 2.25 (concrete pouring) to 3.3 (loading formwork and reinforcement). For laborers, off-site concrete processes consume less energy than on-site concrete processes. This difference in energy expenditure indicates that moving some of the non-significant concrete structural modules from on-site operation to off-site operation will reduce the activity intensity load for the workers, without considering the influences of weather and high temperature. It is assumed that the difference in energy expenditure between on-site and off-site processes would become larger if environmental factors are taken into account since the on-site construction workers are more frequently exposed to extreme weather conditions (e.g., high temperature, snow, wind, and pouring rain).
For energy expended by off-site equipment, the researchers were able to identify the linear relationships between the defined construction levels of automation and unit 76
electrical power consumption. Accordingly, energy expenditure per acre of plant size
increases with increasing levels of automation, which indicates that more equipment is
involved in the production process to replace human workers. As a result, the
researchers explored correlations of energy expended per unit volume (cubic yard) of
concrete production, and of energy expended per unit volume (cubic yard) of precast
concrete modules production. For further computation of on-site and off-site construction energy expenditure, the end-user of the CPQM model only needs to provide the number of cubic yards of concrete modules that are pre-fabricated in the factory, and define the LOA of the concrete plant. Using these input values, the total energy expenditure by the equipment can be evaluated with the model created.
This research study provides essential calculation of energy expended by the off-site
precast concrete process. This calculation is one of the important steps in creating a
quantitative model to compute the construction industrialization rate in the industry.
The LOAs defined in this research study fill the knowledge gap in the construction
industry associated with not having a proper way to identify the level of construction
automation. Many research possibilities exist that relate LOAs and construction
performance (cost, time, safety, quality), and provide guidance to the industry.
Further research is suggested to continuously improve the data analysis for the present
study. Collecting more data is the first priority to improve the accuracy of the research
results and gain confidence that the results are representative of all concrete plants.
With the construction level of automation defined in this paper, future researchers can
explore additional possible correlations of the construction LOA, including but not 77
limited to the impacts of LOA on construction safety, cost, schedule, and quality.
Future research on construction industrialization could focus on the development and application of the model based on the results presented in this paper and the previously published work (Liu and Gambatese 2018). After the model is created and validated, the model could be applied to multiple projects to test the impacts of construction industrialization rate on the four core construction performance criteria. The feasibility and percentage of implementing industrialized construction modules could be another research area in construction.
8.8 DATA AVAILABILITY STATEMENT
Data generated or analyzed during the study are available from the corresponding author by request.
8.9 ACKNOWLEDGEMENT
The researchers would like to express sincere appreciation to the individuals and companies that participated in the survey and provided conveniences and opportunities to the researchers during site observations and data collection. Additionally, the researchers are also immensely grateful to future reviewers of this paper for their insights and recommendations.
8.10 REFERENCES
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(2018). “Exploring the driving factors of construction industrialization development in China”. International journal of environmental research and public health, 15(3), 442. 82 9. MANUSCRIPT #3 Using Construction Process Quantification Model (CPQM) to Evaluation Construction Industrialization Rate: Model Creation, Validation, and Application Ding Liu and John Gambatese To be submitted to Refereed Journals 83 9.1 ABSTRACT The Construction Process Quantification Model (CPQM) was created to evaluate the industrialization rate on construction projects. Construction industrialization refers to the replacement of on-site construction processes by using elements, components, and modules that are precast, manufactured, and/or assembled off-site. With the issues of construction waste and pollution present, industrialized construction is considered to be one of the effective ways to solve the problem. In this paper, the authors focused on construction concrete processes, and calculated the industrialization rate using the amount of energy expenditure for both on-site and off-site construction tasks, and separately evaluated energy expended by laborers and equipment. The quantification model was further validated and applied to multiple construction projects as case studies. An evaluation was conducted while applying this model. The limitations, benefits, feasibility, and future suggestions for the CPQM model are also analyzed during the application process, and presented in this paper. 9.2 INTRODUCTION In this era of rapid development and growth, the construction industry is faced with challenges and new issues, such as pollution (Belayutham, Gonzalez and Yiu 2016), labor shortages (Goh and Goh 2019), safety issues (OSHA 2019), construction waste (Bakchan and Faust 2019), and other issues. New techniques and solutions have been created to solve the critical issues for the industry, and construction industrialization has been mentioned as one of the solutions (Kaplinski 2018) (Zhang, et al. 2016). 84 The concept of construction industrialization (CI) was first proposed in the 1960s and has been developing for more than half a century. CI refers to transferring on-site construction work to an off-site factory to improve quality and reduce cost, time, and safety issues (Grimscheid 2005). In recent years, industrialization in the construction industry has been widely mentioned to solve the worldwide housing crisis, reduce construction waste and pollution, and address other industry concerns. A recent review pointed out a statistical analysis of the literature in the China National Knowledge Infrastructure (CNKI) and Science Citation Index (SCI) from 2001 and 2016. The analysis revealed a rapid increase in scholarly articles related to the keywords “prefabricated,” “modular building,” and “industrialized construction or building” (Huangfu and Xue 2017). The rate of industrialization in construction is considered to be a measurement factor. With more processes produced off-site in factories, construction performance measures (schedule, cost, quality, and safety) might be influenced. However, up to now, there is no formal method for calculating the construction industrialization rate (CIR) and evaluating its impact on projects. In addition, there is a need for such a formal calculation method. The idea of a Construction Process Quantification Model (CPQM) was firstly mentioned in 2018 (Liu and Gambatese 2018). Liu and Gambatese introduced the concept of using energy expenditure to quantify the construction processes, thus defined the CIR by categorizing the amount of energy expended for both on-site construction tasks and off-site construction tasks. Based on previous research, the CPQM model was at a conceptual stage and has not been validated and tested for feasibility. This manuscript aims to unite different research pieces and create the model. 85 The main focus of the manuscript will be put on concrete processes. Validation and application are also tested for this model using statistical methods and case studies. The finalized CPQM model is intended to fill the knowledge gap of the absence of a formal CIR evaluation model, and provide suggestions for the industry to correctly estimate CIR for future project planning. 9.3 LITERATURE REVIEW To create the CPQM model, energy is introduced to combine the many various units used on a construction site, such as cubic yards for concrete, tons for rebar, square feet for platforms, etc. Energy is a common unit that could be used to evaluate all activities and tasks. Human energy expended is measured with calories, and machine energy expenditure can be measured with horsepower or watts. The idea of the CPQM is to evaluate the energy flow and measure the percentages of total energy expended for on- site or off-site construction tasks. To achieve the functions of this quantification model, the following steps need to be created or identified, they are: methods to estimate energy expenditure, list of on- and off-site construction tasks, methods to validate the model, and ways to apply the model. Provided below is a review of literature related to each of these steps. 9.3.1 Estimating Human Energy Expenditure (BMR & PAR) There are ways to evaluate human energy expenditure for human energy research studies. Having workers use wearable devices such as sports watches (Shcherbina, et al. 217) is one of the methods that could be utilized to obtain objective data, and it is one of the most recommended methods by some researchers (ResearchGate 2018). However, 86 the method is not feasible for this research study. The CPQM model aims to evaluate the entire process of construction projects including different building structures and construction delivering methods, which involve hundreds of workers and thousands of construction tasks. A study using wearable devices to measure workers’ energy expenditure on a project could be very expensive and last for years or even decades to finish. Therefore, another method is required. Another method is to collect data from surveys of industry professionals and calculate energy based on the theory of Basal Metabolic Rate (BMR) and Physical Activity Ratio (PAR). BMR represents the number of calories required to keep a human body functioning at rest (ACTIVE 2017). BMR is the minimum energy requirement for human beings to stay alive. It is also known as the human body’s metabolism. Any increase to metabolic movements, such as exercise, will increase the value of a person’s BMR. The BMR value depends on a person’s weight, height, age, and gender. PAR is based on BMR. PAR is the ratio of the physical activity that is used to measure the activity strength level compared to the BMR (Bender 2017). For example, the PAR for activity “driving car to/from work” is 2.0, it means the energy expenditure for driving a car is twice as much as the energy expenditure for sleeping at ease (Energy Expenditure of driving a car = 2*BMR) (Kuriyan, Easwaran and Kurpad 2006). As the energy required to perform an activity increases, the PAR also increases. Previous research (Tontisirin and Haen 2001) identified daily human activities together with their PAR values. According to the research report “Human energy requirements” (2001), daily activity normally has a PAR value range from 1.0 (resting metabolic rate) 87 to more than 8.0 or even higher for very intense activities. The report also mentioned that people could not endure activities with PAR values of more than 4.0 for long periods of time due to the overload exceeding human limitations. To better correlate how construction tasks could be described by daily activities, the researchers converted six of the daily activities from the “Human energy requirements” report (2001) to related construction activities. Take “driving car to/from work” as an example. The converted construction activity is “Driving cars/trucks/heavy equipment”. It is assumed that both the daily activity and the related construction activity require the same amount of energy for human workers to perform. The six converted activities (Liu and Gambatese 2018) identified are assumed to cover all of the workers’ activities while performing construction tasks on a project. 9.3.2 Lists of Construction Design Elements and Tasks (On-site and Off-site) Identifying construction tasks is one of the major and important processes of the present study. The researcher would like to use a construction tasks list that is widely recognized or accepted. To create the list, and the researcher gained access to the structural tasks list and MEP task list created by the Gambatese Research Group. The construction tasks were cited from the RS Means construction database together with the required work- hour per unit of the design elements. RS Means data from Gordian is North America’s leading construction cost database (Gordian 2019). The RS Means data points collected are actively monitored by experienced cost engineers, and the data is widely used and accepted by construction professionals to create budgets, estimates, and cost validations. The list that the researcher used in this research study contains eight primary categories, which are: Foundations and footings, Structural frame, Low slope roofing, Exterior 88 enclosure, Interior construction, Mechanical system, Electrical system, and Plumbing system. The list covers more than 100 design elements with nearly 1000 construction tasks. For the RS Means activity list, all construction tasks included are basically on-site activities. For off-site construction tasks, a much smaller list was created based on an extended literature review on off-site processes. Before deciding which off-site process to be included in this research study, identification of off-site processes is needed. Among all construction processes, most of them were pre-casted or fabricated off-site in a factory. In a steel structure, for example, all steel girders, beams, and columns are pre-fabricated in a factory and assembled on a construction site. Other functional components such as doors and windows, and equipment and piping within an MEP system, are produced in factories. These components of construction works are not included in this research study because they are always fabricated off-site. The researcher chose to focus on the processes that could be either performed on-site or replaced by off-site processes. Therefore, it was decided to focus on concrete processes. Based on current practices, off-site construction process also mainly refer to concrete processes (Mao, et al. 2016), such as prefabrication (Grosskopf, Elliott and Killingsworth 2017) and modularization (Molavi and Barral 2016). The researcher created a detailed list of off-site precast concrete processes based on an extensive review of current literature and online sources. Based on an evaluation of the list, the researcher recognized that precast concrete in off-site precast concrete plants can be identified as either dry-mixed concrete or wet-mixed concrete according to the 89 mixing method (CONQUAS 2018). The difference between the methods is when water is added to the material (CoastalGunite 2015). The precast concrete processes for both mixing methods are listed in Table 9.1. The site observations conducted by the researcher at several concrete plants confirmed the processes to be validated. Furthermore, based on the observations, the researcher found that most precast concrete plants use dry-mixed concrete on standard modules, and use wet-mixed concrete for customized modules. The researcher reached out to the site engineers for the reasons why the mixing methods are used in this way, and received answers like “dry-mix is normally used for small or medium volume modules, and wet-mix is used on large volume modules.” The answers received regarding when and where to use dry-mix or wet-mix are also confirmed to be correct based on industry websites (CoastalGunite 2015). Table 9.1 Construction tasks of precast concrete (wet and dry mixing methods) (CONQUAS 2018) Normal Casting Process for Dry Casting Process (Extrusion Precast Reinforced and Pre- Method) for Precast Hollow Core stressed Concrete Elements (Wet Elements mixed) 1 Assembly of mold Base mold cleaning and preparation 2 Mold cleaning and preparation Pre-stressing strand hauling and tensioning 3 Fixing of rebar/cast in items/pre- Concreting stressed strands 4 Final inspection before casting Curing 5 Concreting De-tensioning of strands 6 Curing Final inspection/ Transfer to storage yard 7 Demolding 8 Final inspection/ Transfer to storage yard 90 9.3.3 Levels of Automation Automation levels have been frequently mentioned and implemented in transportation systems as a factor for evaluating automation. However, for the construction industry, the level of automation is not clearly defined. In Chapter 8.4.5, the author defined the levels of automation in the construction industry based on the taxonomy of ten levels of automation defined by Sheridan and Verplanck (1978). Instead of ten levels of automation, the author defined five levels of construction automation. The levels are: • Level 1 – Construction workers do the whole job with assistance of human- controlled machine; • Level 2 – Sensors embedded to collect data for humans to analyze; • Level 3 – Intelligent automated system assists human workers on data analysis and decision making; • Level 4 – Highly intelligent automated system makes decisions and performs work, and provides report and system warning to human workers when necessary; and • Level 5 - Highly intelligent automated system makes decisions and performs the work without notifying human workers. Based on current technology and in terms of construction processes, the contractors and the workers would not release high levels of authority to automated machines or robotic system to reach the top level of automation (Level 5). The researcher introduced the levels of automation in this research study, and attempted to find correlations between automation levels and possible power consumption on construction sites or in off-site 91 precast concrete plants. Knowing this correlation would benefit the estimation of energy expenditure. Based on the existing research and gap in knowledge, the research question identified for this manuscript is: Is it possible to implement energy expenditure as the unit to quantify the construction industrialization rate for both on-site and off-site construction tasks? 9.4 METHODOLOGY In this research study, the main purpose is to aggregate all the correlated research pieces to build the construction process quantification model. To achieve the aim of this study, the researcher established the research framework illustrated in Figure 9.1. According to Figure 1, three main data collection methods were implemented for data gathering as the first step of the study. Through the literature review, the researcher was able to document the ideas of current practice on construction industrialization and identified the knowledge gaps related to the rate of construction industrialization. For the literature review, the researcher used keywords such as “construction industrialization,” “construction modularization,” “off-site construction processes,” and others to identify related articles. Among all related articles and research studies, no formal calculation method for construction industrialization rate was mentioned. The researcher set filling this knowledge gap as the goal of the research study and conducted extended review on correlated topics. The additional steps to identify construction task lists for both on-site construction activities and off-site precast concrete activities, updated data, and information also rely on literature reviews. 92 • Journal Articles • Conference Proceedings Case Study Project Feasibility of the model • Construction regulations Application • Constr. Equip. Specifications Obstacles of applying the model Literature CPQM Model Creation Review and Validation Defects of the model Cross reference data Survey in Excel spreadsheet Additional data Quantitative Data required to improve the model Site Potential future Observation application Figure 9.1 Research Framework (Manuscript #3) As previously mentioned, the PAR value of each construction task is the main value required to be collected for this study. Since the most frequently recommended method (with wearable devices) for measuring workers’ energy expenditure is not feasible, the researcher decided to conduct surveys of the professional engineers who have multiple years of experience to determine the PAR values. Their perspectives on worker behaviors and time distribution for the six construction activities were recorded based on their best estimation and experience. For instance, for the construction task “pour concrete,” assume a worker is performing this task continuously for eight hours. While performing this task, maybe 1.5 hours were spent “walking at varying paces”, five hours were spent “carrying light loads,” and 1.5 hours were spent on “non-mechanized domestic cleaning, finishing, etc.” The activities that workers performed are the six converted construction activities, which were previously specified by Liu and 93 Gambatese (2018). In addition to the collected time distribution for specific construction tasks, the survey also included demographic questions, and personal opinion questions regarding construction industrialization. Personal perspectives on construction off-site modularization and industrialization are also analyzed in the research. However, survey data is commonly considered to be subjective data, which is not convincible in many circumstances. The researcher will further validate the collected PAR values by comparing survey data and data extracted from on-site videos of real-world projects. For both on-site and off-site construction processes, multiple site visits and observations were conducted by the researcher. With site visits, the researcher is able to collect data from the on-site professional engineers, and identify worker behaviors and time distributions for different construction activities. Additionally, the researcher took videos during the observations with the site manager’s permission. The on-site and off- site videos are used to further validate the survey data on worker PAR values in terms of expended energy estimating. All collected data were recorded and stored in an Excel spreadsheet. A quantitative data analysis method was conducted based on a large amount of data input. Multiple spreadsheets are needed to fill in all functions of the CPQM model. The method of cross- referencing data is also utilized in this study. The researcher used data links and connections among different spreadsheets to ensure the functions of input, output, calculation, supporting data, and other operations are separately addressed in different spreadsheets or Excel files. 94 At the very end of the study, after the CPQM model was created and validated, several case study projects were used on which the model was applied to test the feasibility of the model. The resulting estimated industrialization rate based on the model is demonstrated, together with the construction performance collected from the contractors. While applying the model to different case study projects, the obstacles and issues that occurred during the application process were noted and recorded. Possible revisions and improvement solutions were then created to solve the issues identified in order to ensure the model is justified and feasible. The final output of this study was to determine if there were any defects in the model and if any improvement could be addressed. Future application of the model will also be suggested based on the case study project application process. 9.5 DATA COLLECTION AND RESULTS As mentioned in previous sections, the researcher utilized a literature review, survey, and site observations as methods for this research study. This section describes the data collection processes, analysis of the data, and the results of the analysis. 9.5.1 Survey Results Two separate surveys were conducted to collect industry professionals’ perspectives for on-site construction tasks and off-site precast concrete plant tasks. For the on-site construction task survey, the targeted participants were set to be experienced site engineers and on-site construction professionals. The survey questions were categorized into groups of questions about respondent demographics, construction industrialization information, and construction activity information. A large table that 95 contained all of the on-site construction tasks together with the six “construction activities” was also provided. The participants were asked to fill in the workers’ time distribution for different “construction activities” in the categories of construction tasks that they felt most confident to identify the PAR value for on-site construction tasks. Assuming a worker or a working crew is continuously performing work to construct a design element for 8 hours continuously, the participants were asked to distribute the 8 hours across the physical “construction activities”. For example, for task of “drilling of hole for piles”, we assume the workers or crews are doing the same task for all day 8 hours. During this 8 hours, maybe 2 hours were spent on walking at varying paces without a load, 3 hours were spent at driving equipment, and 3 hours were spent on carrying heavy loads, then we would know how the workers distribute their time while doing this construction task. On-site professionals who took part in the survey would need to distribute the 8-hour time to the six working activities based on his/her experience and knowledge of best practices. Due to the complicated structure and length of time required to complete the survey, the required level of expertise and time availability was not present on every site. As a result, researcher had to go to different construction sites in the Pacific Northwest obtain the necessary data. At the sites, the researcher described to the potential participants the research background and strategies of filling in the tables. The targeted participants for the off-site survey focused on the precast concrete plants. The purpose of the survey was to collect information about the precast concrete plants’ power consumption per unit cubic yard of concrete production, and the workers’ activity strength levels (PAR). Therefore there were two parts for this survey questionnaire: 96 questions to collect plant information, and questions regarding workers’ time distribution while performing tasks for determining the PAR values similar to the first survey. With issues related to survey complexity and length of time required to complete the survey, this survey was also conducted together with an on-site interview and observations. Thanks to the cooperation of the participants, the researcher was able to visit and obtain complete responses from five of the precast concrete plants in the Pacific Northwest area of the United States. A total of more than 50 professionals from the construction industry participated in both surveys and provided feedback for the PAR value data collection and on the survey questionnaires. For the on-site construction survey, the researcher collected a total of 21 full responses to the survey questionnaires. In addition, based on the combination of responses from all 50 participants, three complete datasets were collected for determining PAR values of all construction tasks were received. The number of participants is larger than the number of survey questionnaire responses because the datasets were filled in by a group of site engineers based on their expertise, however, they may have submitted only one survey questionnaire as a group. From the demographic questions, 19 out of 21 respondents identified themselves as a project manager, project engineer, or superintendent. Over half of the respondents (52%) have work experience of more than 20 years in the construction industry. The concept of industrialized construction was widely recognized by the survey participants (76% of respondents), and most of them think industrialization would help with the development of the construction industry. However, the participants indicated that there are concerns and barriers to apply industrialized construction. Most of the responses consider budget 97 issues and policy issues as the main barriers for implementing construction industrialization. Supply chain, or if there is a precast concrete modularization plant nearby, is also the main consideration for general contractors. See figure 9.2 for an illustration of the primary barriers. For the disadvantages that concern the general contractors, a large amount of investment at the beginning was the most frequently cited choice (18 out of 21). With respect to the applicability of industrialization to the construction activities, the researcher included a question using a Likert 5-point scale to collect feedback in which “1” represents not applicable and “5” represents highly applicable. An average score of 3.14 out of 5 was received, with the majority of respondents (11 out of 21) choosing “3-moderate”. The participants worry about the new method because construction industrialization is a process that is suitable for large projects or projects containing a significant amount of repeatable processes. Low dollar value or small projects may not generate the benefits of implementing this method. What are the barriers to perform high level of industrialized project? 12 10 11 11 8 10 6 8 4 6 2 0 Budget provided Policy supported Supply chain Worker training Others, please nearby specify:______ Figure 9.2. Survey results: Barriers to performing industrialization For the data collected for on-site construction design elements and tasks, the researcher was able to receive three complete datasets for all the tasks, each of which includes 98 almost 1,000 construction tasks and more than 5,000 values on worker time distribution to evaluate the PAR values (Liu and Gambatese 2018). The off-site survey focuses on the data collection from the off-site precast concrete plants. It was more difficult for the researcher to obtain feedback from the precast concrete plants than from construction professionals. In the end, the researcher received complete datasets from five different concrete plants within the Pacific Northwest in the United States. 9.5.2 Site Observations Besides conducting surveys for on-site and off-site data collection, the researcher also conducted several site visits and observations. Along with the observations, the researcher was able to take videos and photos with the site managers’ permission. At last, the researcher received tens of hour of videos of construction on-site processes and off-site precast concrete modeling processes. In addition to the self-recorded materials, the researcher also requested a range of photos and time-lapse videos from the Information Services office at Oregon State University (OSU). The videos and photos were further analyzed for model validation. 9.6 MODEL CREATION AND VALIDATION It was assumed that the model should include the following parts: Interface page for users, Results pages, Calculation pages, On-site pages, and Off-site pages. The strategy for compiling all of the information into the model can be simply described as shown in Figure 9.3. 99 Off-site Energy Consumtpion CIR = (Off-site energy consumption)/ Literature Review Surveys Off-site (Entire energy Consumption) On- or Off-site On-site energy On-site Construction Tasks task? Consumption PAR value for construction tasks Unit task work hour requirement (work- Worker energy to Equipment energy hour/unit) be categorized to be categorized Energy consumption performing unit tasks (Calories/unit) Construction Worker Quantity of Types of Average BMR each task Equipment used (Calories/day) Unit Energy = BMR*PAR*work-hour/unit Equipment Types, USER Models and Wattages INPUT Figure 9.3 CPQM Model Simplified Strategy 100 Figure 9.3 shows the paths of how energy expenditure was calculated based on the literature review and survey data. From the literature review, the authors mainly received the construction task list (with work-hours/unit) (Gordian 2019), construction worker average BMR values (Liu and Gambatese 2018), and equipment types, models, and wattages (Caterpillar 2017). The surveys were designated to collect PAR values and off-site precast concrete plant data. With all data received, the researcher calculated the energy expended either for on-site tasks or off-site tasks. For the purposes of the model, the construction industrialization rate is defined as the amount of energy expended on off-site tasks as a percentage of all energy expended for construction on the project. 9.6.1 Obstacles and Assumptions of Model Creation Since all data analysis and categorizing were conducted in Microsoft Excel, the researcher chose an Excel spreadsheet as the foundation to create the model, include coding, calculating, displaying, and data cross-referencing. During the process of creating logic relationships within the data and collecting data for the model, the researcher encountered obstacles and constraints while trying to identify values for different factors. Previous research studies on CPQM concepts have identified various issues while analyzing on-site construction PAR values and energy expenditure, such as using the median value in data analysis among the PAR values collected (Liu and Gambatese 2018), identifying on-site construction equipment (Chapter 7.3), and defining levels of automation (Chapter 8.4.5). While defining the calculation methods for off-site precast concrete tasks, new challenges were met. 101 9.6.1.1 Off-site energy expenditure To evaluate off-site energy expenditure, two main factors are required (see Equation 9.1): cubic yards of concrete consumed in precast modules and energy expended per cubic yard of concrete. Quantity of concrete varies from one project to another, and the quantity is the main factor that influences the industrialization rate. Unit energy expenditure is calculated in the spreadsheet based on collected data. Similar to calculating the energy expenditure for on-site construction tasks, off-site concrete unit energy expenditure is divided into two parts: off-site laborer energy and off-site equipment energy. Energy = Quantity (cy) * Unit Concrete Energy Expenditure (Energy/cy) (9.1) For the laborer energy part, the researcher used the same method of collecting PAR values for evaluation. The reason for using survey data rather than objective data with wearable devices is to be consistent with the on-site laborer energy measurement method. Using multiple methods on different processes might result in incomparable data when building the quantification model. The researcher quantified and analyzed PAR values for previously defined off-site concrete processes, and was able to get workers’ energy expenditure per hour while performing different tasks. To translate the units from energy per hour to energy per cubic yard, another value is required, which is the productivity of off-site precast concrete plants (cy/hour). According to the survey results from the off-site concrete plants, the analysis reveals a statistically significant relationship between concrete consumption and number of employees at the plant. Specifically, the amount of concrete consumed at precast 102 concrete plants is related to the number of employees (employee size) based on limited data size. After analyzing the data, the researcher was not able to get highly significant evidence to prove the correlation between those two factors. A possible reason for this result might be the differences among the levels of automation for the precast concrete plants, which the researcher was not able to identify while on-site. To clarify the names of the variables, concrete consumption of precast concrete plant is defined to be the concrete consumed for producing concrete modules, and employee size refers to all on- site plant workers, including formwork workers, rebar workers, crane operators, and concrete mixing workers. In this way, the researcher was able to predict the potential productivity of off-site concrete workers to be 3.814 man-hr./cy for both the wet-mixed method and dry-mixed method. However, the prediction is based on an important assumption, which is that human laborer productivity does not change. The researcher used the average value based on the data collected to reduce the margin of error. The basic calculation method is displayed in Equation 9.2: Laborer Energy Expenditure to Produce Unit Concrete (Calories/cy) = [Productivity (worker-hour/cy) * BMR (Calories/day) * PAR (activity strength ratio)] / [24 hours/day] (9.2) On the equipment energy aspect, electrical power consumption of each of the precast concrete plants was collected and recorded. With the difference of all the variables (such as plant size, number of employees, model of equipment, levels of automation, and others) among concrete plants, the electrical power consumption is different as well. The researcher tried to find the relationships among plant size, number of employees, 103 levels of automation, and electricity used based on the limited dataset, and did not find significant correlations. Previous efforts identified relationships between and 𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃 𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃 levels of automation, and levels of automation, and and levels of 𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃 𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃 𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶 𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃 𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆 automation, and were able to identify the significant trends (Chapter 8.5.4). The researcher identified precast concrete plant equipment energy expenditure based on the correlation factors after removing non-production expended energy. In this case, non- production energy expended refers to the energy expended in the plant office, such as printing, and office lighting, heating, and cooling. Accordingly a U.S. office building typically spends nearly 29% of its operating expenses on utilities (E Source 2010), so the authors assumed that for a precast concrete plant, only 71% of the energy is used in concrete production. Thus 29% of the concrete plants’ electric power consumption was deducted from the energy prediction. 9.6.1.2 Different Energy Magnitude Originally, the researcher tried to separately define energy expended by construction laborers and equipment. This separation enabled the energy values to be subdivided into four different categories: artisanship (on-site by laborer), automation (on-site by equipment), pre-fabrication (off-site by labor), and industrialization (off-site by equipment). The researcher could then evaluate the rate by identifying the percentages of energy flows in each different category. The theory looks good conceptually until the model was finished and ready to be applied. While applying the model, the researcher found that human energy expenditure and equipment energy expenditure is not in the same order of magnitude. According to 104 EnergyEducation (Hanania, et al. 2018), it is calculated that human energy expenditure is roughly equal to 100 watts based on a daily energy diet. However, the heavy equipment on construction sites is powered by tens or hundreds of Kilowatts. When defining energy in the artisanship and pre-fabrication categories, due to the much smaller amount of energy expenditure by humans compared to equipment, the percentages of those two categories could not reach 1%, and are incomparable with the equipment energy categories of automation and industrialization. The researcher finally decided to combine all energy expended by both laborers and equipment for on-site and off-site tasks, respectively. Lastly, the researcher defined the construction industrialization rate (CIR) to be the percentage of off-site energy expenditure in terms of the entire energy expenditure for measured construction tasks, as shown in Figure 9.3. The result shows similar percentages between on-site vs. off- site construction and automation (on-site machines) vs. industrialization (off-site machines) due to the small amount of laborer energy expenditure compared to the machine energy. The researcher decided to keep the construction laborers’ energy expenditure rather than delete this component because the values of laborer energy expenditure could present the differences between on-site and off-site laborer energy distribution. And, in future research, the change of laborer expenditure could in some way influence the CIR value. 9.6.1.3 Levels of automation identification The researcher defined five different levels for construction automation (refer to Chapter 8.4.5), and applied the levels to find the relationships with energy expenditure 105 factors associated with the precast concrete plant. There are clear boundaries among the five levels, and it is easy for the users to define levels of automation for concrete plants and construction sites. The identification of levels of automation ranks construction tasks from Level 1 – worker intensive to Level 5 – highly intelligent automation systems. Based on current technology and techniques, a highly intelligent automated system is not feasible and cannot be achieved. The automation levels for precast concrete plants were identified by the researcher and participants themselves. Most of the data points were located in Level 1 and Level 2 of construction automation. No concrete plants were identified to be in Level 5 of automation. The researcher would have to assume a linear correlation in terms of electric power consumption versus levels of automation. Actual relationships might be different, but for now there is no other way to predict the energy expenditure for highly intelligent automated precast concrete plant systems. 9.6.2 Model validation As previously mentioned, the researcher collected subjective data through surveys to evaluate the PAR values for each of the construction processes. To test if the collected data could be used to represent the actual situation, the researcher went a step further to validate the data by analyzing recorded videos of projects. During site observations of different on-site construction projects and off-site precast concrete plants, the researcher took videos and photos while interviewing the site engineers and observing activities under the authorization of the site managers. Based on interviews and observations collected from different sites over the past years, the researcher collected over 20 hours of videos (over 60 gigabytes) of construction worker 106 activities while performing different tasks, including on-site concrete pouring, wall panel installation, MEP system installation, as well as off-site activities including formwork installation, dry-mixed and wet-mixed concrete pouring, and concrete module finishing processes. In addition to the site-recorded videos, the researcher was able to obtain additional videos and photos of past projects through the Oregon State University (OSU) information service. Oregon State University established a webcam system across the campus for marketing, educational, and entertainment purposes (OSU 2019) since the late 20th century, and makes it publicly accessible on the OSU website. Since then, for every newly built project, one or more webcam(s) are installed to show the progress of the new building. Initially, webcams installed could only send back a range of photos every 1 minute or every 5 minutes. However, with the fast developing technology, the cameras installed in recent years can produce live broadcasted videos. To save the recordings, the server saves an image from each camera every 1 minute, and builds time- lapse videos every night. Daily time-lapse videos are saved in the server for several weeks before being merged into weekly and monthly time-lapse videos. The researcher obtained 29 time-lapse videos of 29 past construction projects throughout OSU, and received a range of photos (every 5 minutes) of 15 of the projects. The range of photos covers the whole process of constructing the projects from the day the project broke ground to the day of completion. 107 To validate the PAR data, the researcher selected one of the on-site construction processes and five of the off-site concrete plant processes for evaluation. A description of the validation process is provided below: • For on-site validation, the researcher picked the concrete pouring process as the focus of the evaluation. From the survey, the PAR data for all on-site construction tasks that related to the concrete pouring process were selected, such as “concreting of pile (PAR=2.75)”, “pouring of concrete for beams and slab (PAR= 2.36)”, etc. The list of PAR values on “concrete pouring” processes provides the range, median, and average of the subjective data received from the survey. From the video, the researcher analyzed the videos that contain the same process. The timeline of the video was recorded, and the researcher analyzed each of the concrete workers and noted each of the construction activities (defined by Liu and Gambatese, 2018) that the workers were performing at that point in time. To more efficiently evaluate the video data, the researcher set the time point to be every 2 minutes for the video and 5 minutes for the range of photos. The PAR values were calculated based on the workers’ time distribution on different construction activities. Figures 9.4 and 9.5 show a sample of the video images along with the noted worker activities and timeline. 108 Figure 9.4 Video Analysis Demo Figure 9.4 illustrates how the researcher analyzed the video. For the video frames analyzed at different timeline points, each worker was identified by specific characteristics (e.g., hat color, tool used, or clothing colors) in order to track the workers from one image to the next. The physical activity strength level was correlated to the task the concrete workers were performing as the PAR indicator of the worker at that time point. A cycle of 12 minutes was selected due to the activity duration and video length (Figure 9.5), and the researcher analyzed physical strength levels of each of the construction workers task at two minute intervals within the cycle to estimate the PAR value of the workers while performing on-site concrete processes. 109 Figure 9.5 Video Analysis with Identified Timeline Frame 110 As a result of the video and photo analysis, the data received from the video shows an average PAR value for the concrete pouring process is 2.556, slightly lower (by 1.2%) than the PAR value calculated from the survey data (2.586). The median PAR value of 2.500 is slightly lower (by 0.52%) than the survey data PAR value (2.513). The range of PAR values from the video analysis is larger than the range collected from the survey. A potential reason for this difference is the different task assignment (e.g., some workers are assigned to hold and direct the pumping pipe, some are assigned to do the vibrating, and some are performing concrete leveling work). Figure 9.6 shows more details about the analysis results. Figure 9.6 PAR validation (on-site concrete process) • For the off-site PAR value validation, the researcher randomly picked five off- site processes in the precast concrete plant to focus on. The selected processes were: “Concreting (dry-mixed)”, “Concreting (wet-mixed)”, “Fixing of 111 rebar/cast in items/pre-stressed strands”, “Finishing”, and “Inspection and transport to yard”. For all five of the off-site processes, the PAR values collected through the survey were noted, and compared with the values calculated from the video analysis. In the video analysis, videos from more than three different precast concrete plants were analyzed to ensure the data is representative. A comparison between survey data and video data was conducted, as shown in Figure 9.7. From the figure, it can be seen that the differences between survey data and video data were within 5%, and therefore, the collected survey data could be considered as sufficiently valid data to build the model. Figure 9.7 PAR validation (off-site concrete process) 9.7 MODEL APPLICATION After overcoming all of the obstacles with reasonable assumptions, the researcher was able to create the CPQM model using Excel spreadsheets with cross-references. In this 112 section of the dissertation, the model is primarily described and applied to case study projects to identify potential defects and solutions. 9.7.1 Model Interface Originally, the researcher put different spreadsheets in different Excel files. When trying to calculate a value, the program needs to read data from other files, which slowed the speed of calculations. Finally, the researcher combined all the spreadsheets in one file to complete the model. A total of seven spreadsheets were created and combined, including “INTERFACE PAGE”, “Construction levels of automation”, “RESULT PAGE”, “Off-site precast concrete”, “PAR on-site laborer”, “On-site machine” and “SupportData#1”. The users need to input task quantities and on-site equipment types and models, then identify the construction level of automation for the precast concrete plant from the first two spreadsheets. The resulting CIR is automatically calculated and visualized in the “RESULT PAGE”. Figures 9.8 and 9.9 provide depictions of the model input and output pages. 113 Figure 9.8 CPQM model - Input page 114 Figure 9.9 CPQM model - Output page 115 The “Off-site precast concrete” page calculates all the values for off-site concrete production and modularization energy expenditure. The page automatically reads all the input numbers from the “INPUT PAGE” and calculates the results based on the previously mentioned process. Finally, a hardcopy of the results may be printed. The method described above is similar to the PAR on-site laborer and on-site machine energy calculation, multiple spreadsheets read input numbers and perform calculations separately. All final results are cited on the “RESULT PAGE” to allow for a final summary and figure illustration. The final printed results are: overall on-site and overall off-site energy expenditure, on-site and off-site laborer energy expenditure, and on-site and off-site equipment energy use. 9.7.2 Application Case Study 1 The first case study project was located in Oregon and was built for classroom and office use. The general contractor is a US company headquartered in Portland, Oregon. The project contains 15 new classrooms and an auditorium, the largest of which can seat at most 400 people, with a total of 134,000 square feet (12449 square meters). The estimating documents were prepared in 2003, and the project was finished in 2016. Figure 9.10 is the photo taken in 2014 and shows the concrete pumping process. 116 Figure 9.10 Range Photo of Case Study 1 (OSU 2014) The researcher requested from the general contractor the estimating documents with detailed quantities listed. While analyzing the on-site concrete pumping quantity and off-site precast concrete volume, the researcher found that this project was not highly industrialized. Due to the large interior space requirements, the main structural elements on this project consisted of structural steel for the framing and concrete shear walls. Precast concrete elements were mainly concrete foundations, aesthetic features, and walkway curbs. As a result, the calculated CIR is estimated to be 21.61%, implying that for all concrete processes, only 21.61% of the total energy was expended off-site in a factory. With respect to construction worker energy expenditure, 97.77% of the energy was expended on-site and 2.23% off-site. This ratio implies that for the concrete process, 97.77% of the labor force was assigned on-site and 2.23% was assigned off-site. Similarly for the equipment, 78.13% of the total energy was expended on-site and 21.87% was expended off-site, while the off-site concrete plant was identified to be at Level 2 117 in terms of automation. The results of the Case Study 1 analysis are shown in Figure 9.11. Figure 9.11 Output of case study 1 application 9.7.3 Application Case Study 2 The second case study project was also located in Oregon. The building is located on a school campus, and contains a public lounge, classrooms, study rooms, café, and offices. The project was completed and opened in 2014, with a gross area of 100,000 square feet (9290 square meters). The project cost more than $50 million dollars. It is reported that the project was finished on time and on budget. The range photo taken by the OSU webcam in 2015 is shown in Figure 9.12. 118 Figure 9.12 Range Photo of Case Study 2 (OSU 2015) Based on the room distribution in this building and the structural frame on the project, only a few large spans exist. The main structural frame is concrete, so in terms of concrete consumption, this building consumes a larger volume than the first case study project though the amount of constructed area is smaller. In this project, large precast concrete elements were requested from the off-site precast concrete plant. Based on the project estimating book received from the general contractor for the project, major off- site concrete modules from this project were precast bases, precast sills, precast headers, and precast copings. The model application result is shown in Figure 9.13. 119 Figure 9.13 Output of case study 2 application According to the CPQM model application, the industrialization rate for this project is estimated to be 34.22%. For this project, due to the undefined automation level for the precast concrete plant, the researcher tried Level 1 and Level 2 of automation. The values shown in Figure 9.9 are based on the assumption of the lowest automation level (Level 1). When changing the automation level to Level 2, the off-site energy rate increases significantly to 61.12%. This result indicates another limitation of the model, and is discussed in detail in the next section, “Findings from Application”. 9.7.4 Findings from Application 9.7.4.1 Feasibility During the model application process, the researcher tested the feasibility and identified several defects. An overall evaluation indicated that the model is feasible to be applied. The researcher was able to receive values needed and visualize the values based on a series of calculations. Besides real case application, the researcher also checked the link connections among different spreadsheets to ensure all calculation steps are included in the model and the results were sent to the “result page”. Random values for quantities were entered in the “input page” to test embedded formulas and print results. Based on 120 the randomized test, the researcher did not recognize any major defects, which indicates that the model is ready for application of more construction projects for further testing and evaluation. 9.7.4.2 Model theoretical basis CIR is different for different projects. For all off-site precast concrete elements or modules, on-site assembly and installation are required, which require a large amount of energy for lifting, hoisting, and assembling. The larger the volume of the module, the larger amount of horsepower in the on-site equipment would be needed. So theoretically, the construction industrialization rate could never be 100%. Actually, the researcher assumed that there is a maximum value of the CIR that all construction projects would not be able to exceed; this maximum CIR value is assumed to be smaller than 100%.Equation 9.3 shows the CIR calculation method, on-site energy expenditure always exists, which makes CIR unable to reach 100%. CIR (%) = [Off-site Energy Expenditure (Mega Joule)] / [Off-site +On-site Energy Expenditure (Mega Joule)] (9.3) In the CPQM model, the researcher used cubic yards as the major unit to quantify precast concrete modules and elements. For on-site construction processes, some estimating books may use square feet to describe areas of concrete foundation and slabs, or use vertical linear feet to describe columns and piles. The researcher normally applies these parts of elements based on project drawings to find the other dimensions to identify the overall volume of concrete consumed. For those not specified, the 121 researcher would use the widely accepted values from manuals and standards, such as 6-inch thickness for slabs, 2.75-inch thickness for precast sills, etc. 9.7.4.3 Minor defects First, identification of levels of construction for off-site precast concrete plants is important. The energy expenditure value of changing from Level 1 and Level 2 of construction automation differs substantially. With this fact, the researcher questioned that the possible relationship between levels of automation and electric power consumption might not be linear as assumed. However, this relationship requires much more data to prove, and is considered to be the major improvement needed for this model. Second, while applying construction project cases, no foundation pile information was included in the estimating book, so concrete pile parts were not included during the evaluation. With the two case studies, the researcher did not recognize the types of piles each project used. In the CPQM model created, three concrete piles were identified: precast concrete piles, bored concrete piles, and driven concrete piles. With piles included, the CIR value might be slightly higher or lower. While analyzing the concrete components for different projects, the estimating books are the major materials that the researcher used. To analyze all concrete components for different projects, the researcher recognized that there some components were completely produced in precast concrete plants, and on-site process are only installation and placement. Those components are not structurally related, and therefore were not originally included in the CPQM model. These elements include: precast cane bench, 122 precast base, sill headers, etc. To categorize those types of concrete elements, the researcher created several customized off-site concrete elements in the “interface page” for the case when the concrete used is not related to the structural frame or foundations. The users could enter element names and quantities used for these elements to make sure all concrete elements were considered when computing the CIR of the project. While different general contractors use different estimating methods, the description of the same element sometimes varies in different estimating books. For example, with the two case study projects, in the column descriptions, the estimator used square feet as the unit. Normally, linear footage of the piles or columns together with the cross-section area is widely accepted. Using square feet to describe the columns, the researcher would have to use the best guess and would need to check the project drawings to confirm, which sometimes makes the element not applicable or makes the application process more complicated. 9.8 LIMITATIONS The CPQM model included an extensive list of values and large volumes of calculation formulas on various processes. To make sure every process is involved in the computing method, the researcher had to make assumptions, which would definitely result in limitations for this study. This section presents several limitations for creating and applying the model. 9.8.1 Small data size Overall, the researcher conducted two surveys, six on-site construction projects observations, and five off-site precast concrete plant observations. Additionally, the 123 researcher recorded more than 20 hours of site worker operations and working processes, accessed hundreds of gigabytes of materials that consist of approximately twenty project time-lapse videos and thousands of site photos. Hence, to fully accurately build the CPQM model, the data size is still too small. All data collected was from the Pacific Northwest area of the US. It is difficult to say that the model could represent all construction projects nationally or globally. The small data size for off-site concrete plants makes the assumptions unconvincing, such as the correlations between automation level and power consumption. After data analysis during the application process, changing the automation level from Level 1 to Level 2 will result in the CIR rate greatly increased from 20% to 64%. This impact indicates that the correlation data and assumption might be incorrect. To verify the assumption and revise the model if needed, more data points are required to produce more accurate correlation curves. 9.8.2 Productivity Issues During the off-site energy expenditure computing, the researcher introduced productivity values to calculate energy expenditure for off-site construction concrete plant laborers. Based on the statistical tests with limited data, the researcher was able to determine the final value of worker productivity to be 3.814 cy/hour in terms of concrete production and modularization. The value was set to be a constant number in this study, and does not change when levels of automation became higher. Theoretically setting this value as a constant may not be correct, since the purpose of implementing higher levels of automation in the concrete plant is to increase productivity. However, with 124 limited data, the researcher was not able to find any correlations between the identified automation levels and worker productivity. 9.8.3 Inaccurate Energy Estimating in Precast Concrete Plant Electrical power consumption has many impacting variables, such as the number of employees, and the size of the office building, and even varies in different areas and locations. Cold and hot weather impacts the power consumption substantially while trying to cure concrete. As previously mentioned, the data collected for precast concrete plants was mainly from the Pacific Northwest area; the values collected may not represent the common value nationally. 9.9 CONCLUSIONS AND RECOMMENDATIONS In this manuscript, the authors demonstrated the processes used to create the CPQM model, along with its validation and application. The CPQM model is aimed at filling the knowledge gap associated with the absence of a formal method to compute construction industrialization rate. The researcher created the CPQM model based on the energy expenditure flows, and afterward measured on-site energy expenditure and off-site energy expenditure to determine the industrialization rate as percentages of off- site energy expended in terms of the entire project energy expenditure. Based on the processes used for CPQM creation, validation, and application, the researcher was able to answer the research question. The results indicate that energy expenditure can be used to quantify construction tasks in terms of CIR. This manuscript summarizes the entire PhD dissertation. The model creation process largely depends on the data collected and analyzed from Manuscripts #1 and #2. The first two manuscripts 125 were created as the data collection processes of the overall dissertation, though both manuscripts demonstrated individual results and conclusions. This manuscript combined research segments from the first two manuscripts, and achieved the overall goal of creating the CPQM model with the unit of energy expenditure. Accordingly, the researcher collected subjective data through surveys and validated the collected responses through video and photo comparison. After the model is completed and validated, the researcher applied the model using a randomized number input test to find the missing links and connections among the spreadsheet. After the model was confirmed to be feasible, the researcher applied the model to two real-world projects for further model analysis of defects and potential improvements. The results of the two case study project applications are also graphically presented to show the construction industrialization rate. Based on the application process, minor defects in the model were recognized and revised to improve the feasibility of the model. For those issues that could not be resolved with current techniques and technologies, the authors acknowledged the issues in the limitations section, and plan to address the issues in future research. In terms of future research recommendation, the CPQM model has great potential and not only limited to computing the CIR. Using the model, the final output reveals the energy distributions for both workers and equipment, and for both on-site and off-site tasks. This output could be applied to future research studies of human resource allocation and equipment resource allocation and leveling. As for the CIR values, construction performance measures such as safety, quality, schedule, and cost could be 126 investigated with respect to their correlations to the CIR. The CIR value could be a factor used to provide guidance to new projects and as an evaluation factor for current projects in terms of construction performance. 9.10 REFERENCES ACTIVE. 2017. "BMR Calculator." ACTIVE. July 5. 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Accessed March 23, 2019. https://www.researchgate.net/post/How_to_measure_labors_energy_expenditu re_while_working. Shcherbina, A., CM. Mattsson, D. Waggott, H. Salisbury, JW. Christle, T. Hastie, MT. Wheeler, and EA. Ashley. 217. "Accuracy in Wrist-Worn, Sensor-Based Measurements of Heart Rate and Energy Expenditure in a Diverse Cohort." Journal of Personalized Medicine (MDPI) 7 (3): 1-12. Sheridan, T.B., and W. L. Verplanck. 1978. Human and computer control of undersea teleoperators. Technical Report, Cambridge: MIT Man-Machine Laboratory. Tontisirin, Kraisid, and Hartwig de Haen. 2001. Human energy requirements. Rome: Food and Agriculture Organization of the United Nations (FAO). http://www.fao.org/docrep/007/y5686e/y5686e00.htm. Zhang, Jinyue, Yating Long, Siquan Lv, and Yunchao Xiang. 2016. "BIM-enabled Modular and Industrialized COnstruction in China." Procedia Engineering (Elsevier) 145: 1456-1461 130 10. DISCUSSION Note: Chapter 10, 11, 12 and 13 demonstrate the overall Discussion, Conclusion, Limitations, and Future research recommendations for the research study. For each manuscript (listed in Chapter 6, 7, 8 and 9), individual results, conclusions and contributions to knowledge are listed. After conducting the three-step study, the research successfully answered the research questions. The process of creating the CPQM model was a process of going from unknown to known, and a process of finding problems and solving problems. During the processes undertaken, obstacles and constraints were met in creating and applying the model. This section discusses the findings from the research study and methods to resolve or improve them. 10.1 Answers to research questions As presented in Chapter 3, several research questions were posed based on the knowledge gaps identified by the literature reviews. During the process of solving the research questions, the researcher was able to follow a process to fill the knowledge gaps and change the answers from unknown to known. The first research question asked if energy could be used as a common unit for evaluating construction tasks. In the first manuscript, the researcher introduced BMR and PAR values to measure construction worker energy expenditure, and in the additional content of Manuscript #1, multiple equipment types and models were listed to estimate the equipment energy expenditure while performing on-site construction 131 tasks. Based on the convertibility of all units that express energy, the researcher finally converted all energy units to megajoules (1 megajoule = 1x106 joules) to quantify the construction tasks for CIR estimating purposes. Hence, energy can be used as a common unit to evaluate construction tasks when calculating CIR. Second, to organize all construction tasks into different categories, the researcher tried several methods at first, but categorizing all of the thousands of construction tasks is a large amount of work and the final results are subjective. The method selected and used in this research study is to categorize energy flows rather than categorize construction tasks. All energy expended for a construction project flows to four different categories: artisanship (on-site by laborers), automation (on-site by equipment), pre-fabrication (off-site by laborers), and industrialization (off-site by equipment). For each construction task, multiple equipment or laborers may be involved for either on-site tasks or off-site tasks, or both. It is hard to put each construction task into any of the categories, however, energy expended could be easily identified as flowing to the four categories. Research Question 3 asks about the method used to evaluate construction worker energy expenditure. The most direct option for measuring worker energy expenditure is to have the workers use wearable devices for real-time energy expenditure data collection. However, it was not feasible in this research study given that there are approximately one thousand construction tasks to evaluate. Since the cycle of a construction project could last for years and continual monitoring would be required, it would be nearly impossible to obtain field data on the entire process. Consequently, the researcher 132 selected a much more applicable way to collect laborers’ energy expenditures. BMR and PAR were introduced in this research study, and were mentioned and widely recognized in the human metabolic studies that could be used to measure energy expenditure for humans. The disadvantage of using those two terms is that all data should be collected based on site professionals’ experiences and perspectives through surveys, which makes the data validating process extremely important in later studies. The fourth question asked if all construction elements or components could be pre- fabricated off-site in factories. The answer is yes, and no. The purpose of asking this question was to identify which construction design elements should be considered in the construction industrialization processes. The answer yes to this question is when all construction components could be pre-fabricated or pre-assembled off-site in a factory. The component in this case could be as small as a part of the Mechanical, Electrical and Plumbing (MEP) system, or could be as large as entire pre-assembled rooms or buildings. On the other hand, saying no to this question may be because for some construction components, the components must be pre-fabricated off-site in a factory and assembled on-site, such as windows, doors, and steel beams, columns, and girders. In this research study, the researcher aims to focus on the construction processes that could be either performed on-site or off-site in factories. The process that meets this criterion is the concrete pouring process, for both concrete structures and aesthetic concrete components. Next, the researcher questioned how to present the CPQM for implementation in practice. To present the CPQM model with a large database and complex calculations, 133 the preferred way is to create an app or software, which may require related knowledge of computer science and coding. Since the data were sorted and analyzed in MS Excel, the researcher realized that with the built-in functions of Excel, cross-referencing between the spreadsheets could be used to achieve the goal of data reading, computing, and printing results. With the cross-referencing method, the researcher is able to keep one spreadsheet as the input page and display it for the end users, while all calculations and data reading could be automatically completed in other spreadsheets by analyzing data from the input page. The final result could also be displayed in a results spreadsheet with values and diagrams automatically changed based on the input. In this way, the researcher was able to provide a user-friendly interface to make the model application easier for users. Last but not least, is the question of how to validate the CPQM model. As previously mentioned, to evaluate the energy expenditure for construction laborers, the concepts of BMR and PAR were introduced. To get the activity strength levels of construction tasks, the researcher collected personal perspectives from site engineers and professionals on workers’ time distributions associated with different “construction activities”. With limited data collected, the PAR values received may be subjective and biased. To test if the collected data are valid, the researcher conducted a comparison between the survey data and video data. The video was recorded during the site observations by the researcher, and the detailed comparison is described in Chapter 9, section 9.6.2. For model feasibility testing and case study application, the results are described in detail in section 9.7. The results reveal that the model is feasible for application and use on real construction projects for either research or project guidance purposes. 134 10.2 Statistical methods statement In the research study, the researcher conducted data analyses in the manuscripts. This section discusses the statistical methods that the researcher selected for data analysis processes in the three manuscripts. In the first manuscript, construction laborers’ working time distribution data for the PAR calculation were collected. With all the data collected, the researcher chose to use the median value instead of the mean value to estimate the worker’s time distribution while performing different construction tasks. There are two reasons that the author considered when choosing the median instead of the mean value: 1) small data size (Freidlin and Gastwirth 2000); 2) potential outliers (Howell, et al. 1998). First, the data size is limited as a result of the researcher being able to obtain only three complete datasets for all construction tasks (around 1000 tasks). For some specific construction tasks, there could be as many as five or six responses. In this case, using the median value would be more appropriate. The second reason is related to the potential outlier issues. When more data was collected, there would be a potential issue of outliers. In the validation process presented in section 9.6.2, the data collected from videos (lefthand data in Figure 9.4) clearly illustrates the issue of outliers. To avoid the impacts of outliers, the median is more appropriate than the mean value. In the second manuscript, goal was to find the relationship between the automation levels of concrete plants and their energy expenditure per unit production of concrete. Originally, the researcher intended to conduct an F-test to evaluate if there is a linear correlation between the two variables However, due to the limited data size, a statistical 135 analysis would not be feasible, and the p-value could not prove any significant evidence (Ghasemi and Zahediasl 2012). As a result, the strategy was modified to simply present an estimated model for energy expended for producing unit cubic yard of concrete in terms of different levels of automation, and shown in section 8.5.4. Since the collected data points are not located in all levels of automation (only in Levels 1, 2 and 4), for automation Levels 3 and 5, no data points were available to represent the energy expenditure levels. Since levels of automation are discrete variables, not continuous, the researcher left the energy expenditure values blank for those Levels 3 and 5, and will update the values in the future when new data points are received. 136 11. CONCLUSIONS According to the purpose of this research study, the researcher aimed to create a quantification model for calculating the construction industrialization rate of projects. Three steps were identified to achieve the overall goal to fill the knowledge gap of having no formal CIR calculation methods. The three steps of the research study have the same overall objective and were drafted separately as three manuscripts. The manuscripts have tight connections with each other, and yet presented independent conclusions and contributions to the knowledge. This section describes overall conclusions based on the results obtained from each manuscript and acknowledges the contributions of this research study to the body of knowledge. 11.1 Manuscript #1 The primary research goal of the first manuscript is to identify the energy expenditure for on-site construction workers and equipment while performing different construction tasks. In order to obtain the energy expended for construction laborers, the concepts of BMR and PAR were introduced, and surveys conducted to collect perspectives from site-engineers and construction professionals. As a result, the researcher concluded that unit energy expenditure varies from 121.4 Calories/hour (0.51 Megajoules/hour) to 232.7 Calories/hour (0.97 Megajoules/hour) for female construction workers, and from 153.6 Calories/hour (0.64 Megajoules/hour) to 294.6 Calories/hour (1.23 Megajoules/hour) for male construction workers while performing different on-site construction tasks. 137 In addition to the laborers’ energy expenditure, a list of all on-site construction equipment was identified based on extensive review of current literature and online sources. The list contains a total of 14 types of equipment with more than 40 different models, which were able to cover all construction tasks. With the quantities input for different construction elements, the energy expenditure could be estimated with the work-hour and average horsepower of the equipment. As a contribution to the body of knowledge, this manuscript proves that energy expenditure can be used to evaluate construction tasks, and provides a theoretical basis for the CPQM model to quantify the construction industrialization rate. The manuscript also demonstrated those high strength level tasks for construction workers, which provides suggestive guidance to the construction workers while performing those tasks on site. 11.2 Manuscript #2 The second manuscript focuses on the off-site precast concrete processes. Data associated with off-site precast concrete workers’ energy expenditure and precast concrete plant electric power consumption were obtained through a survey, site observations, and interviews. Based on the data collected, the researcher concluded that construction workers’ energy expenditure while performing off-site concrete tasks is lower than that of on-site concrete tasks, which indicates that moving non-significant concrete structural modules from on-site operation to off-site operation will reduce the activity intensity load for the workers, without considering the influences of being exposed to extreme weather conditions and high temperatures. 138 Additionally, to better analyze the energy expended by off-site precast concrete plants, five levels of automation (LoAs) were created based on theories of automation levels in other industries (section 8.4.5). The levels categorized on-site projects and off-site construction module plants from the lowest level (Level 1– all human work with simple machines assistant), middle level (Level 3 – automated system would collect and analyze data from built-on-site sensors and provide suggestive processes), and to the highest level (Level 5 – highly intelligent automated system controls the whole processes and notify human laborers only when necessary). Based on current technology and techniques, it would be impossible for construction sites or off-site construction plants to implement highly intelligent automated systems. The researcher identified the high level of automation as a reserved value, and believes that someday in the near future, it could be possible to highly rely on automated system for construction processes. To the body of knowledge, this manuscript contributes the levels of construction automation, which was not found mentioned in previous literature. This manuscript filled the knowledge gap in identifying construction LoAs. After analyzing off-site construction worker’s energy expenditures, this manuscript illustrated solid evidence that for construction workers, off-site concrete tasks consume less energy than an on- site construction concrete process without considering influences of weather and temperature. 139 11.3 Manuscript #3 The third manuscript summarizes the data collected and analyzed from Manuscripts #1 and #2, and demonstrates the processes used to create the CPQM model along with its validation and application. The researcher created the CPQM model based on energy expenditure flows, and afterward measured on-site energy expenditure and off-site energy expenditure to determine the industrialization rate as percentages of off-site energy expended in terms of the entire project energy expenditure. Manuscript #3 combined research segments from the first two manuscripts, and achieved the overall goal of creating the CPQM model with the unit of energy expenditure. Based on the processes used for CPQM creation, validation, and application, the researcher was able to answer the last two research questions: how to create a user- friendly interface, and find a way to validate the model. The researcher collected subjective data through surveys and validated the collected responses through video and photo comparison. After the model is completed and validated, the researcher applied the model using a randomized number input test to find the missing links and connections among the spreadsheet. To create a user-friendly interface, the researcher implemented the cross-referencing method in the MS Excel software. Multiple spreadsheets were created with different functions assigned (input, output, data support, and calculation), and the model was applied based on two case study projects. During the research processes, the researcher identified minor defects in the CPQM model and revised them to improve the feasibility of the model. For those issues that could not be resolved with current techniques and technologies, the researcher 140 acknowledged the issues in the limitations sections and plans to address the issues in future research studies. 11.4 General Conclusions In conclusion, this dissertation presents the research and findings associated with creating a quantification model using energy expenditure for CIR evaluation. To achieve the goal, the researcher implemented three research steps (presented in three manuscripts), and separately discovered the energy expenditure by laborers and equipment for both on-site and off-site construction activities within Manuscripts #1 and #2. The third manuscript summarized the results of the first two manuscripts, and built the CPQM model in MS Excel with cross-referencing functions and connections among the spreadsheets. Subjective data collected from the survey was validated through real project videos and photos, to make sure the subjective data is more than 95% accurate compared to objective values. To provide a better experience of applying the model, the researcher created a user-friendly interface with multiple spreadsheets, and tested the connections and internal links with randomized number testing to make sure the model is feasible for application to real projects. Two case study project applications were conducted to prove the feasibility and diagnose minor defects and issues. The preliminary model now is ready to use on construction projects for CIR estimating. To ensure a high level of accuracy of the estimated CIR value, more data collection and further improvement are necessary. All three manuscripts have a close connection with each other, and presented a clear path of the research steps. However, each of the manuscripts presented individual results 141 and contribution to the body of knowledge. The first manuscript was submitted to and presented at the ASCE Construction Research Congress (CRC) 2018 held at New Orleans, LA. Due to the long term data collection and validation, Manuscript #2 was not submit to the PCI journal until April 2019. The third manuscript is now finished and will be submitted to a refereed journal. 11.5 Contributions to the Body of Knowledge The concept of construction industrialization (CI) was proposed in the 1960s and has been developing for more than half a century. CI refers to the transfer of on-site construction work to an off-site factory to improve quality and reduce cost, time, and safety issues (Grimscheid 2005). In recent years, industrialization in construction industry has been widely mentioned as a possible solution to the worldwide housing crisis, presence of construction waste and pollution, and other industry concerns. The benefits of industrialized construction on a project, such as reducing construction costs, time, and waste, have been shown and are widely accepted by the industry. Literature describing current research studies mentions the shortcomings of construction industrialization, such as informal standards (Huangfu and Xue 2017), implementation of industrial construction components, design of industrialized modules (Grimscheid 2005), and structural stability. However, based on Yerkes-Dodson Law (1908), “performance increases with physiological or mental arousal, but only up to a point; when levels of arousal become too high, performance decreases.” It is assumed that there may be similar relationships between rate of industrialization and construction performance (schedule, cost, quality, and safety). When the industrialization rate increases, the effects of one or more of the issues of behind schedule, over budget, low 142 quality and high injury rate could be reduced. The hypothesis is that when the CIR kept increasing and passed over the optimal point, the performance of construction criteria will not be positively affected. To prove the hypothesis, a model for estimating the CIR value is needed. Before this research study, no formal standard or calculation model was available to estimate the industrialization rate of construction. The researcher implemented energy expenditure to quantify construction tasks in order to estimate the CIR. The development of the quantification model fills the knowledge gaps associated with formulating and operationalizing the concept of CIR and increasing the accuracy of CIR estimating. The higher level of accuracy of CIR estimating provides a theoretical basis for future estimation of possible correlation between the CIR value and construction performance indicators. For the CIR application, the created CPQM model connected construction process adjustment and CIR values in terms of construction performance improvement. For the current construction industry, multiple methods have been created to solve the issues and challenges the construction industry is faced with. Construction industrialization is considered to be one of the solutions to solve the worldwide housing crisis, high levels of construction waste and pollution, and other industry concerns. For the body of knowledge, the CPQM model connects the existing information of current construction issues and one of the potential solutions. Additionally, this research study contributes a theory to the body of knowledge for CIR evaluation, which enables future research studies on construction industrialization related topics. 143 12. LIMITATIONS The creation of the CPQM model included processes of data collection, analysis, classification, and computing. Though the researcher conducted extensive literature reviews and multiple model tests, there are still limitations in this research study that limit its generalization to all parts of the industry. Several limitations, as described above, are a small data size and biased data points (sections 6.6, 8.6, and 9.8.1), multiple general situation-based assumptions (sections 9.8.2 and 9.8.3), and imperfections of the model (section 9.7.4.3). However, conscious effort was taken to improve the research outcome by validating collected data, testing and applying the CPQM model, and implementing multiple research methodologies in the research processes where necessary. The CPQM model is currently completed and has undergone preliminary validation. It is in its preliminary phase of application. Further improvements and enrichment of the model are necessaries based on more future applied projects and updated datasets. 144 13. FUTURE RESEARCH RECOMMENDATIONS This research study provides essential calculation of energy expended for both on-site and off-site construction tasks. The quantification model of construction processes was established as a supportive part of the knowledge of construction industrialization. For future research study on the topic, two avenues are recommended: demonstrated, improvement of current CPQM model, and future application of the CPQM model. 13.1 Model Improvement As previously described in Chapter 12, the CPQM model is in its preliminary phase of application, more data points and continuously updated construction tasks are required to improve the accuracy of the estimated CIR value and strengthen the hypothesis of the model methodologies. Firstly, the PAR values were evaluated from site professionals’ personal perspectives through surveys, and are based on the participants’ best judgment and experiences. Though the data was validated through project video data analysis, the data collected through surveys is always considered to be subjective. To increase the accuracy of the collected PAR values for construction laborers’ energy expenditure estimating, more construction professionals are needed to provide their opinions and perspectives. On the other hand, construction laborers’ energy expenditure data could be collected via wearable devices. The process of collecting objective data with electronic wearable devices could last for years and be costly, and will need cooperation from general contractors and workers. 145 The energy expenditure of off-site precast concrete equipment was estimated based on the amount of electricity use reported on a concrete plant’s electric power bill after removing the percentage of office power usage. The energy estimation is not accurate with limited data obtained. It is assumed that with the big data input available, the model could provide a more accurate value of CIR. Additionally, the off-site equipment that consumes fossil fuels instead of electric power was not included in the estimation. To improve the accuracy of the model in terms of off-site equipment estimation, individual equipment types and equipment should be evaluated and listed. The process could be similar to the on-site equipment identification processes described in Chapter 7. 13.2 Potential Relationships Industrialized construction is considered to be one of the solutions to solve the issues and challenges that the construction industry is currently faced with. The creation of the CPQM model provides theoretical support for CIR evaluation. Based on the hypothesis presented in Figure 1.1 in Chapter 1, it is assumed that there is a correlation between the rate of construction industrialization and construction performance criteria such as schedule, cost, safety, and quality. Correlation studies between the criteria could be potential research interests in the future. Conducting such correlation studies would require a large amount of case study applications of the model, together with recording the performance criteria associated with each construction project. One more issue to be clarified, based on the current CPQM model, the scale of energy expenditure and the scale of industrialization are different. It is assumed that small changes in energy expended may result in large impacts to industrialization. Future 146 research could also focus on this research area, to explore the scale differences and how much do changes in energy influence the CIR values. 13.3 CIR Evaluation and Guidance When the model is ready to be applied to more construction projects and the range of CIR value that represents an optimal construction performance is identified, the value of CIR could be applied on completed and future projects for the evaluation and prediction of construction performance. For completed projects, the CIR values could be used as one of the measurement factors for project performance evaluation. On the other hand, the results of evaluation could be recorded as historical data for future project prediction. Future construction projects could implement the CIR as one of the solutions for budget control, schedule planning, and safety and quality management. Owners will be able to reasonably adjust the CIR for their projects to obtain an optimal outcome based on different situations. 147 14. BIBLIOGRAPHY Note: This bibliography part only includes resources cited in Chapter 1 to 5, and Chapter 10 to 13. For sources cited from Chapter 6 to 9, individual reference lists are included in each of the manuscripts. Arashpour, Mehrdad, Ron Wakefield, Babak Abbsi, E.W.M. Lee, and James Minas. 2016. "Off-site construction optimization: Sequencing multiple job classes with time constraints." Automation in Construction 262-270. Arashpour, Mehrdad, Ron Wakefield, Nick Blismas, and James Minas. 2014. "Optimization of process integration and multi-skilled resource utilization in off-site construction." Automation in Construction 72-80. Blismas, Nick, Christine Pasquire, and Alistair Gibb. 2006. "Benefit evaluation for off- site production in construction." Construction Management and Economics 24 (2): 121-130. CROWN. 2017. 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"Normality Tests for Statistical Analysis: A Guide for Non-Statisticians." International Journal of Endocrinology and Metabolism 10 (2): 486-489. Gibb, Alistair G.F. 1999. Off-site fabrication: Prefabrication, Pre-assembly and Modularisation. New York: John Wiley & Sons, Inc. Gibb, Alistair, and Frank Isack. 2003. "Re-engineering through pre-assembly: client expectations and drivers." Building Research & Information (Taylor & Francis) 31: 146-160. Goh, Edward, and Martin Loosemore. 2016. "The impacts of industrialization on construction subcontractors: a resource-based view." Construction Management and Economics (Talor & Francis) 288-304. Goh, Matthew, and Yang Miang Goh. 2019. "Lean production theory-based simulation of modular construction processes." Automation in Construction (Elsevier) 101: 227-244. 149 Gordian. 2019. About RSMeans data. March 23. Accessed March 23, 2019. https://www.rsmeans.com/info/contact/about-us.aspx. Grimscheid, Gerhard. 2005. "Industrialization in Building Construction." Understanding the Construction Business and Companies in the New Millennium: Proceedings of the 11th Joint CIB International Symposium: Combining Forces-Advancing Facilities Management and Construction through Innovation. Helsinki, Finland: VTT Technical Research Centre of Finland and RILUniversity of West Indies. 427-441. Ha, Quang, M. Santos, Quang Nguyen, D. Rye, and H. Durrant-Whyte. 2002. "Robotic excavation in construction automation." IEEE Robotics & Automation Magazine (IEEE) 9 (1): 20-28. Hampson, Keith D., and Peter Brandon. 2004. Construction 2020 - A vision for Australia's Property and Construction Industry. CRC Construction Innovation Programme. Howell, D.C., M Rogier, V Yzerbyt, and Y Bestgen. 1998. Statistical methods in human sciences. New York: Wadsworth. Huangfu, Wenbo, and Xiaolong Xue. 2017. "A Review on Current Research about Construction Industrialization." International Conference on Construction and Real Estate Management (ICCREM) 2017. Guangzhou, China: 2017 ASCE. 139-146. 150 Ji, Yingbo, Fadong Zhu, Hongxian Li, and Mohamed Al-Hussein. 2017. "Construction Industrialization in China: Current Profile and the Prediction." Applied Sciences 7 (2): 180. Kong, Liulin, Heng Li, Hanbin Luo, Lieyun Ding, and Xiaoling Zhang. 2018. "Sustainable performance of just-in-time (JIT) management in time-dependent batch delivery scheduling of precast construction." Journal of Cleaner Production (Elsevier) 193: 684-701. Nadim, Wafaa, and Jack S. Goulding. 2011. "Offsite production: a model for building down barriers: A European construction industry perspective." Engineering, Construction and Architectural Management (Emerald Group Publishing Limited) 18 (1): 82-101. OSHA. 2019. Commonly Used Statistics. March 18. Accessed March 18, 2019. https://www.osha.gov/oshstats/commonstats.html. Rubio-Romero, J.C., M. Suarez-Cebador, and Jesus Abad. 2014. "Modeling injury rates as a function of industrialized versus on-site construction techniques." Accident Analysis & Prevention (Elsevier) 66: 8-14. Samuelson, Paul A., and William D. Nordhaus. 2001. Microeconomics. 17th. New York, NY: McGraw-Hill Education. Silverman, Dennis. 2019. Energy Units and Conversions. April 8. Accessed April 8, 2019. https://www.physics.uci.edu/~silverma/units.html. 151 Tahir, M. M., N.A. Haron, A. H. Alias, A. N. Harun, and I. B. Muhammad. 2018. "Improving Cost and Time Control in Construction Using Building Information Model (BIM): A Review." Science & Technology (Pertanika) 26 (1): 21-36. Tam, Vivian W.Y., C.M. Tam, S.X. Zeng, and William C.Y. Ng. 2007. "Towards adoption of prefabrication in construction." Building and Environment (Elsevier) 42 (10): 3642-3654. Wang, Jun, Weizhuo Sun, Wenchi Shou, Xiangyu Wang, Changzhi Wu, Heap-Yih Chong, Yan Liu, and Cenfei Sun. 2015. "Integrating BIM and LiDAR for Real- Time Construction Quality Control." 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APPENDIX 15.1 Appendix A IRB Approval Letter of Research Surveys 153 154 15.2 Appendix B Survey Questionnaire – On-site Construction Task Survey 155 Construction Work Task and Worker Activity Time Distribution Survey Dear Participant, We would like to thank you for taking the time to participate in this survey entitled “Construction Work Task and Worker Activity Time Distribution Survey." Your responses to this survey and personal information provided will be kept confidential, used only for academic purposes related to the study, and will not be distributed to the public. All identifying information connecting respondents to their responses will be removed as part of the data collection process. Publications generated from the research study will not include any information that can be used to identify respondents. If you have any questions about the survey, please contact the researchers listed below. If you have questions about your rights or welfare as a survey participant, please contact the Oregon State University Institutional Review Board (IRB) Office at 541-737-8008, or by email at [email protected]. Research Team: Ding Liu, Civil and Construction Engineering, Oregon State University, 101 Kearney Hall, Corvallis, OR 97331; Cell-phone: (541) 979-7286; E-mail: [email protected] John Gambatese, Civil and Construction Engineering, Oregon State University, 101 Kearney Hall, Corvallis, OR 97331; Cell-phone: (541) 737-8913; E-mail: [email protected] Acknowledgment: By continuing the survey, I have read the above description of the research. If I had questions or would like additional information, I contacted the researchers and had all of my questions answered to my satisfaction. I agree to voluntarily participate in this research. By answering the survey questions and responding to this survey, I affirm that I have read the above information, agree to participate in the research, and am at least 18 years of age or older. Survey Questions 156 Demographic Information Q1. What kind of company do you work for? o General Contractor o Sub-Contractor o Supervisor o Owner o Others, please identify:______Q2. How large is your company (number of employees)? o Less than 50 o 50 ~ 100 o 100 ~ 200 o 200 ~ 500 o More than 500 Q3. What is your position/title? o Project Manager o Project Engineer o Superintendent o Others, please identify:______Q4. How many years of experience do you have in the construction industry? o Less than 2 o 2 ~ 5 o 6 ~ 10 o 10 ~ 20 o More than 20 Construction Industrialization Information “Construction Industrialization” refers to moving some of the construction activities from on-site to off-site, and largely using machines and equipment to finish the pre- fabrication processes. Compared to pre-fabrication, industrialization is more automated and relies more on machines rather than human labor. Q5. Have you ever heard of construction industrialization? o Yes o No o I am not sure 157 Q5a. If yes, do you think construction industrialization helps with the development of the construction industry? o Yes o No o It depends, please specify: ______Q6. What are the barriers if you were asked to perform a project that contained a high level of industrialization? Please select all that apply. o Budget provided o Policy supported o Supply chain nearby o Worker training o Others, please specify: ______Q7. What makes it easier to perform industrialization? o Owner support o Budget provided o Policy supported o Supply chain nearby o Worker training o Others, please specify: ______Q8. What disadvantages do you think exist in performing industrialized construction projects? o Large amount of investment at the very start o Less control of the project at first o Worker training costs time and money o Others, please specify: ______Q9. To what extent do you think industrialization is applicable to the construction activities? 1-Not 3-Moderate 5-Highly Applicable applicable o o o o o Construction Activity Information Q10. What part(s) of building construction work are you most familiar with? Please select all that apply. o Foundations &Footings o Structural Frame 158 o Roofing o Exterior Enclosure o Interior Construction o Mechanical System o Electrical System o Plumbing System Q11. Do you think those parts of the building construction work listed above cover all of the construction work? o Yes o No o Not sure Q12. For those selected areas of construction work that you are most familiar with, please fill in the time distribution required for each worker activity associated with each of the construction tasks using the table(s) below. Final Summarized Question Q13. Please share any other suggestions that you may have regarding the construction activities. ____ If you have any questions or want to learn more about our research, please feel free to reach us at: [email protected] or [email protected] Thanks again! 159 Sample Format of the Table Top-left Box: Shows the construction activity with which you have expertise (selected from previous survey question) First Column: Design element and related construction tasks. First Row: Workers’ possible activities during the entire construction processes for the specified design element. How does the table work? Assume an 8-hour work cycle for each construction task. We would like to measure how much time is distributed to different worker activities for each work task. For example, if construction task 1 lasts for 8 continuous hours, workers will need to walk at varying paces without a load for 2 hours, carry light loads for 5 hours, and use light tools for 1 hour. What you need to do: Use your best judgment to fill in the blanks for each construction tasks with numbers from 1 to 8 representing the number of hours during an 8-hour shift in which that activity takes place. Make sure the sum of the numbers for each construction task totals to 8 hours. If the worker activity is not necessary for some of the tasks, leave the box blank. Worker Activities Explanation Explanatio Carrying heavy Non- Sitting (office Driving Standing, Walking at loads, heavy load mechanized work, discussion n cars/trucks carrying varying paces Sum BUILDING SYSTEMS /heavy work, work with domestic meeting, task without a load light loads equipment heavy tools activity distribution) Eleme Design Element No.1 nt Constr. Construction task 1 2 5 1 8 Tasks Construction task 2 3 2 3 8 Eleme Design Element No.1 nt Construction task 1 8 Constr. Construction task 2 8 Tasks Construction task 3 8 Eleme Design Element No. … nt Constr. Construction task 1 8 Tasks Construction task … 8 160 15.3 Appendix C Survey Questionnaire – Off-site Precast Concrete Plant Survey 161 Off-site precast concrete plant workers’ behavior survey Dear Participant, We would like to thank you for taking the time to participate in this survey entitled “Off-site precast concrete plant workers’ behavior survey." Your responses to this survey and personal information provided will be kept confidential, used only for academic purposes related to the study, and will not be distributed to the public. All identifying information connecting respondents to their responses will be removed as part of the data collection process. Publications generated from the research study will not include any information that can be used to identify respondents. If you have any questions about the survey, please contact the researchers listed below. If you have questions about your rights or welfare as a survey participant, please contact the Oregon State University Institutional Review Board (IRB) Office at 541-737-8008, or by email at [email protected]. Research Team: Ding Liu, Civil and Construction Engineering, Oregon State University, 101 Kearney Hall, Corvallis, OR 97331; Cell-phone: (541) 979-7286; E-mail: [email protected] John Gambatese, Civil and Construction Engineering, Oregon State University, 101 Kearney Hall, Corvallis, OR 97331; Cell-phone: (541) 737-8913; E- mail: [email protected] Acknowledgment: By continuing the survey, I have read the above description of the research. If I had questions or would like additional information, I contacted the researchers and had all of my questions answered to my satisfaction. I agree to voluntarily participate in this research. By answering the survey questions and responding to this survey, I affirm that I have read the above information, agree to participate in the research, and am at least 18 years of age or older. 162 Survey Questions Demographic Questions Q1. What is your position/title? • President • Project Engineer • Superintendent • Site manager • Others, please specify: ______ Q2. In which state/country is your precast concrete plant located? • US state (Initials): ______• Overseas country: ______• Others, please specify: ______ Q3. How many years of experience do you have in this industry? • Less than 2 years • 2 ~ 5 years • 5 ~ 10 years • 10 ~ 20 years • More than 20 years Precast Concrete Plant Information Q4. What kind of precast concrete products does the plant you work for produce? Select all that apply. o Pipe o Utility trench o Utility vault o Slab o Wall panel o Customized module o Others, please specify: ______ Q5. Please indicate the total number of workers in the plant who work in each of the following areas: Wet plant operations: ______ Dry plant operations: ______ Rebar crews: ______ Formwork crews: ______ Plant yard (excluding storage yards): ______ Other, please specify: (______): ______163 Other, please specify: (______): ______ Q6. Please indicate the size (approximate square feet) of your precast concrete plant (exclude storage yards and office buildings). ______ Q7. Electrical power expenditure of the equipment and/or plant: Please indicate the total amount of electrical power consumption for the equipment used in the plant (per week, month, or year). If the amount of electrical power consumption for just the equipment is not available, please indicate the total expenditure for the entire building. Electrical power consumption for equipment (in Kwh): ______Electrical power consumption for entire plant (in Kwh): ______ Q8. Production rate of the plant: How many cubic yards (CY) of concrete could the concrete mixer produce each day? ______On average, how many cubic yards (CY) of concrete are used on a typical day to make precast concrete elements/modules? ______ Q9. What types and models of the machines do you use? And how many hours of time is the machine operating per day (assume 8-hour working shift)? Models Operating time (in hours) Gantry cranes Concrete mixer Dry concrete system Rebar station Others, please specify:______Others, please specify:______Others, please specify:______ 164 Personal Perspectives Q10. To what extent do you think your plant is automated or human-directed? • Automated (computer controlled, production line, etc.) • Human-directed (humans work with assistance of machines) Q11. In your experience, for each of the following products, what is the approximate total cycle time of producing one typical module in your plant? Also, please enter the size of product that your plant produces. Module size Cycle time (min) Pipe Utility trench Utility vault Slab Wall panel Customized module Other, please specify:______ Q12 and Q13 are in attached EXCEL file “Q12 and Q13 of Off-site worker behavior survey”. (Screenshots) 165 Q12. Time distribution of product types (Screenshot from Excel) 166 Q13. Off-site behavior data collection (Screenshot from Excel) 167 15.4 Appendix D Hand-written Notes and Calculations for On-site Equipment Identification (Scanned) 168 169 170 171 172 15.5 Appendix E Screenshots of CPQM model 173 Figure E.1. INPUT PAGE 174 Figure E.2 Construction Levels of Automation Selection Page 175 Figure E.3 OUTPUT PAGE 176 Figure E.4 Off-site Precast Concrete Process Calculation Page 177 Figure E.5 On-site Construction Laborer Energy Expenditure Page 178 Figure E.6 Estimation of PAR Values for On-site Construction Tasks (Sample part) 179 15.6 Appendix F On-site Construction Tasks List Used in CPQM (Systems and Design Elements included) (Construction tasks excluded) 180 SYSTEM/ COMPONENT DESIGN FEATURE/ ELEMENT FOUNDATIONS & FOOTINGS 1.0 STEEL H-PILES 2.0 STEEL PIPE PILES 3.0 PRECAST CONCRETE PILES 4.0 BORED CONCRETE PILES 5.0 DRIVEN CONCRETE PILES 6.0 PILE CAP 8.0 DRILLED SHAFT/ DRILLED PIER FOUNDATION 9.0 DRILLED CAISSON FOUNDATION 10.0 ISOLATED COLUMN FOOTING/ SPREAD FOOTING 11.0 WALL/ STRIP FOOTING 12.0 MAT FOUNDATION 13.0 SLAB ON GRADE/ SLAB ON GROUND 181 STRUCTURAL FRAME A. COLUMNS 1.0 STRUCTURAL STEEL COLUMNS 2.0 BUILT-UP STEEL COLUMNS 3.0 CAST IN PLACE CONCRETE SQUARE COLUMNS 3.1 CAST IN PLACE CONCRETE ROUND COLUMNS 4.0 PRECAST CONCRETE COLUMNS B. SHEAR WALLS 5.0 CAST IN PLACE CONCRETE WALLS C. BEAMS 6.0 STRUCTURAL STEEL BEAMS 7.0 PLATE GIRDERS/ BUILT-UP GIRDERS 8.0 PRECAST BEAMS D. MONOLITHIC FRAME AND SLAB 9.0 ONE WAY SOLID SLAB 10.0 TWO WAY SOLID SLAB 11.0 FLAT PLATE SLAB 12.0 ONE WAY JOIST FLOOR SLAB 13.0 TWO WAY JOIST FLOOR SLAB (WAFFLE SLAB) 14.0 POST-TENSIONED BEAM AND SLAB E. SLABS AND DECKS 15.0 HOLLOW-CORE PLANK SLABS 16.0 CELLULAR DECKS 17.0 OPEN FLOOR DECKING 18.0 STEEL FORM DECKING(open and cellular) 19.0 COMPOSITE DECK 20.0 STEEL ROOF DECK F. STAIRS 21.0 PRECAST STAIRS 22.0 REINFORCED CONCRETE STAIRS 23.0 STEEL STAIRS (CONCRETE FILLED TREAD PANS) 24.0 STEEL STAIRS (PRECAST CONCRETE TREADS) 182 LOW SLOPE ROOFING A. ROOF COVERINGS 1.0 BUILT-UP ROOF MEMBRANE ROOFING 2.0 MODIFIED BITUMEN ROOF MEMBRANE ROOFING 3.0 SBS MODIFIED BITUMEN MEMBRANE ROOFING 4.0 APP MODIFIED BITUMEN MEMBRANE ROOFING 5.0 SINGLE-PLY ROOF MEMBRANE ROOFING (Fully adhered system) 6.0 SINGLE-PLY ROOF MEMBRANE (Mechanically fastened attached system) B. ROOF OPENINGS AND SPECIALTIES 7.0 SMOKE HATCH 8.0 ROOF HATCH 9.0 SKYLIGHTS C. ROOF EDGES AND EXPANSION JOINTS 10.0 BUILDING SEPARATION JOINT 11.0 EDGE GUARD 12.0 PARAPHET WALL 183 EXTERIOR ENCLOSURE A. INFILL/ BACK-UP WALL 1.0 BACK-UP WALL (Steel Studs) 2.0 BACK-UP WALL (CMU Wall) B. CURTAIN WALLS/ VENEER WALLS/ CLADDING 3.0 SHEET METAL PANELS 4.0 INSULATED METAL PANELS 5.0 BRICK VENEER CURTAIN WALL [Backup wall-steel studs w/ exterior insulation] 6.0 BRICK VENEER CURTAIN WALL [Backup wall-steel studs w/o exterior insulation] 7.0 BRICK VENEER CURTAIN WALL [Backup wall-CMU] 8.0 PREFABRICATED BRICK PANEL CURTAIN WALLS 9.0 STONE PANELS MOUNTED ON STEEL SUB FRAME 10.0 MONOLITHIC STONE CLADDING PANELS 11.0 STONE CLADDING ON STEEL TRUSSES/ PREFABRICATED STONE CURTAIN WALLS 12.0 STONE-HONEYCOMB PANELS (THIN STONE CLADDING) 13.0 PREFABRICATED STONE HONEYCOMB CURTAIN WALL 14.0 PRECAST CONCRETE CURTAIN WALLS 15.0 GLASS FIBER-REINFORCED CONCRETE (GFRC) CURTAIN WALL 16.0 STUCCO FINISH [Backup wall-steel studs] 17.0 STUCCO FINISH [Backup wall- CMU] 18.0 EXTERIOR INSULATION AND FINISH SYSTEM (EIFS) 19.0 GLASS CURTAIN WALLS/ GLASS ALUMINUM CURTAIN WALLS [UNITIZED SYSTEM] 20.0 GLASS CURTAIN WALLS/ GLASS ALUMINUM CURTAIN WALLS [STICK-BUILT SYSTEM] 21.0 GLASS CURTAIN WALLS/ GLASS ALUMINUM CURTAIN WALLS [UNIT AND MULLION SYSTEM] 22.0 GLASS CURTAIN WALLS/ GLASS ALUMINUM CURTAIN WALLS [PANEL SYSTEM] 23.0 GLASS CURTAIN WALLS/ GLASS ALUMINUM CURTAIN WALLS [COLUMN COVER AND SPANDREL SYSTEM] C. EXTERIOR DOORS D. EXTERIOR WINDOWS 184 INTERIOR CONSTRUCTION A. PARTITION WALLS 1.0 FRAMED PARTITIONS (Steel stud wall) 2.0 WALL PARTITIONS (CMU wall) 3.0 GLASS MASONRY WALL 4.0 FOLDING ACCORDIAN 5.0 MOVABLE AND BORROW LITES (DEMOUNTABLE) B. INTERIOR WALL FINISHING 6.0 DRYWALL [Framed partition] 7.0 DRYWALL [CMU wall partition] 8.0 GYPSUM PLASTER [Framed partition] 9.0 PORTLAND CEMENT PLASTER [Framed partition] 10.0 GYPSUM PLASTER [CMU Wall partition] 11.0 PORTLAND CEMENT PLASTER [CMU Wall partition] 12.0 VENEER PLASTER C. CEILINGS 13.0 SUSPENDED ACOUSTICAL CEILINGS 14.0 SUSPENDED GYPSUM BOARD CEILINGS 15.0 SUSPENDED PLASTER CEILINGS (GYPSUM PLASTER) 16.0 SUSPENDED PLASTER CEILINGS (PORTLAND CEMENT PLASTER) D. FLOORING 17.0 CERAMIC TILE FLOORING 18.0 STONE PANEL FLOORING 19.0 TERRAZZO FLOORING 20.0 RAISED ACCESS FLOORING 21.0 CARPET FLOORING 22.0 RESILIENT FLOORING E. INTERIOR DOORS 21.0 Aluminum doors 185 MECHANICAL SYSTEM A. Heating, Ventilation, and Air-Conditioning (HVAC) 1.0 FURNACE 2.0 BOILER 3.0 HEAT PUMP/SPLIT SYSTEM AIR CONDITIONER 4.0 ROOFTOP UNIT, RTU (PACKAGED TERMINAL AIR CONDITIONER, PTAC, OR "PACKAGE UNIT") 5.0 MULTIPLE PACKAGED TERMINAL AIR CONDITIONERS, OTACS (ie., WINDOW AND WALL UNITS) 6.0 CHILLER 7.0 COOLING TOWER 8.0 PUMP 9.0 COPPER PIPES FOR HYDRONIC SYSTEMS 10.0 AIR HANDLING UNITS, AHUs 11.0 TERMINAL UNITS, Tus, MIXING/VARIABLE AIR VOLUME, VAV, BOXES, and DAMPERS 12.0 SHEET METAL DUCTWORK 13.0 FLEXIBLE DUCTWORK 14.0 DIFFUSERS, REGISTERS, GRILLS 186 ELECTRICAL SYSTEM A. ELECTRICAL 1.0 UNDERGROUND UTILITIES 2.0 SYSTEM GROUNDING 3.0 BACK-UP GENERATORS 4.0 TRANSFORMERS 5.0 SWITCHGEAR 6.0 PANELBOARDS 7.0 CONDUIT AND BOXES (KIMCTOPM, PULL, SWITCH, RECEPTACLE) 8.0 WIRE/CABLE TRAYS 9.0 WIRE AND CABLE 10.0 SWITCHES AND RECEPTACLES 11.0 LIGHT FIXTURES 187 PLUMBING SYSTEM A. FIRE SUPPRESSION 1.0 PUMP 2.0 STEEL PIPE 3.0 COPPER PIPE 4.0 CPVC PIPE B. PLUMBING 1.0 THERMOPLASTIC PIPE (PVC, CPVC, ABS) 2.0 STEEL PIPE 3.0 CAST IRON SOIL PIPE 4.0 COPPER PIPE 5.0 CROSS-LINKED POLYETHYLENE (PEX) TUBING 6.0 PIPE INSULATION 7.0 WATER HEATING EQUIPMENT 8.0 FIXTURES (sinks, faucets, toilets, urinals, shower heads, etc.)