MULTIVARIATE OPTIMIZATION OF NEIGHBORHOOD SCALE PROBLEMS FOR ECONOMIC, ENVIRONMENTAL, AND SOCIAL SUSTAINABILITY

BY

GRANT NORMAN MOSEY

DISSERTATION

Submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy in Architecture in the Graduate College of the University of Illinois at Urbana-Champaign, 2020

Urbana, Illinois

Doctoral Committee:

Professor Brian Deal, Chair Professor Mohamed Boubekri Associate Professor Richard K. Strand Assistant Professor Yun Kyu Yi

ABSTRACT

This thesis begins by arguing that the architectural profession has fallen out of balance.

I contend that the series of objectives which compete for the architect’s attention have been gradually subsumed by economic concerns. As a means of empirically seeking to restore a balance, a method is proposed for quantitatively determining the trade-offs between the social, economic, and environmental sustainability of an architectural problem. The method is tested on a neighborhood-scale mega-development. This scale of built environment intervention falls between the building scale work of architects and the city scale of urban designers and geographers.

The selected design intervention is in Chicago, Illinois with variables including the number of buildings, their use, and the height of each type of building. Solutions are optimized for Social, Economic, and/or Environmentally sustainability outcomes using a single-objective genomic algorithm. A multi-objective genomic algorithm is then utilized to evaluate all three sustainability objectives simultaneously. Solutions to the chosen problem are proposed and contextualized within the “restoring balance” framework discussed above. The outcomes appear to show that such a process can be efficacious in quantitativley balancing the sustainability based trade-offs implicit to the design process.

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TABLE OF CONTENTS

LIST OF TABLES AND FIGURES ...... v

CHAPTER 1: PROBLEM STATEMENT ...... 1

1.1 INTRODUCTION ...... 1

1.2 THE PLANNER’S TRIANGLE ...... 2

1.3 THE PRESENT METHOD ...... 4

1.4 ARCHITECTURE AND THE ECONOMIC NODE ...... 4

1.5 ARCHITECTURE AND ENVIRONMENT ...... 9

1.6 ARCHITECTURE AND SOCIAL JUSTICE ...... 14

1.7 THE LACK OF PRESENT BALANCE BETWEEN NODES ...... 18

CHAPTER 2: LITERATURE REVIEW ...... 20

2.1 OPTIMIZATION ...... 20

2.2 THE GENETIC ALGORITHM ...... 20

2.3 ADDRESSING MULTIPLE OBJECTIVES AND THE PARETO SURFACE ...... 22

2.4 OPTIMIZATION IN BUILDINGS AND ARCHITECTURE ...... 23

2.5 OPPORTUNITIES FOR EXPANSION ...... 27

CHAPTER 3: METHODS ...... 30

3.1 EXPLORING A NEW METHOD ...... 30

3.2 CASE DEFINITION ...... 30

3.3 VARIABLES ...... 33

3.4 ECONOMIC OBJECTIVE ...... 36

3.5 ENVIRONMENTAL OBJECTIVE ...... 50

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3.6 SOCIAL OBJECTIVE ...... 59

3.7 CONSTRAINTS ...... 69

3.8 MODEL SCENARIOS ...... 70

CHAPTER 4: RESULTS ...... 72

4.1 MODEL PARAMETERS ...... 72

4.2 PRE-SCENARIO INFORMATION ...... 73

4.3 SCENARIO A: ECONOMIC OPTIMUM ...... 78

4.4 SCENARIO B: ENVIRONMENTAL OPTIMUM ...... 81

4.5 SCENARIO C: SOCIAL OPTIMUM ...... 84

4.6 SCENARIO D: MULTI-OBJECTIVE OPTIMUMS ...... 87

4.7 SCENARIO VISUALIZATIONS ...... 98

CHAPTER 5: CONCLUSIONS ...... 100

5.1 OUTCOME EVALUATION ...... 100

5.2 LIMITATIONS ...... 102

5.3 APPLICABILITY TO DESIGNERS ...... 103

5.4 FURTHER RESEARCH ...... 104

BIBLIOGRAPHY ...... 107

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LIST OF TABLES AND FIGURES

Figure 1: Planners' Triangle, example from Campbell and Fainsten (2003) ...... 3

Figure 2: Trade-off's Out of Balance. Adapted from the Planners' Triangle ...... 18

Figure 3: Pareto Front (Left) and Surface (Right) Image adapted from the University of Sheffield https://www.sheffield.ac.uk/acse/staff/peter_fleming/intromo ...... 23

Figure 4: Levels of Digital Explorations in Architecture: Adapted from Carl Steinitz (2012) ...... 31

Figure 5: Six Proposed Mega-Developments in Chicago. Adapted from Google Earth and

Photos by Respective Developers ...... 32

Figure 6: Cost Premium per Height using Multiplier Method ...... 39

Figure 7: Per-Unit-Floor Area Costs by Building Type and Height ...... 39

Figure 8: Relationship between Unit Height and Rental Price per unit Floor Area for Three

Buildings ...... 42

Figure 9: Relationship between Unit Height and Sale Price per unit floor area for Three

Buildings ...... 44

Figure 10: Floor Count vs. Floor Plate Efficiency with data from Barton and Watts (2013) and

Sev and Ozgen (2009) ...... 48

Figure 11: Greenhouse Gas Emissions per Unit Floor Area for Subject Building Types ...... 53

Figure 12: Embodied Energy Premium For Height, Calculated based on Values from Forabachi et al (2013) ...... 55

Figure 13: Net Floor Area per Occupant, not accounting for vacancy loss ...... 57

Figure 14: Use Fraction multiplies applied to fuel consumption of each building type ...... 58

Figure 15: Employees for a 1,000,000 NSF Building of Each Use Type ...... 66

Figure 16: Per Unit Floor Area Profit per Building by Type and Height ...... 74

Figure 17: Return On Investment by Building Type and Height ...... 75

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Figure 18: Total Greenhouse Gas Emissions by Building Type and Height ...... 77

Figure 19: Per Unit Floor Area Greenhouse Gas Emissions by Building Type and Height ...... 77

Figure 20: Best and Average Solutions by Generation for Single-Objective Economic Model .. 79

Figure 21: Best Solution by Generation for Single-Objective Economic Model ...... 79

Figure 22: Optimum Solution for Single-Objective Economic Model ...... 80

Figure 23: Best and Average Solutions by Generation for Single-Objective Environmental Model

...... 82

Figure 24: Best Solution by Generation for Single-Objective Environmental Model ...... 82

Figure 25: Optimum Solution for Single-Objective Environmental Model ...... 83

Figure 26: Best and Average Solutions for Single-Objective Social Model ...... 85

Figure 27: Best Solution for Single-Objective Social Model ...... 85

Figure 28: Optimum Solution for Single-Objective Social Model ...... 86

Figure 29: Histograms showing Distributions of First Rank Solutions by Objective Cost Function

...... 88

Figure 30: Non-Dominated Solution Set by Economic and Environmental Performance ...... 90

Figure 31: Non-Dominated Solution Set by Economic and Social Performance ...... 91

Figure 32: Non-Dominated Solution Set by Environmental and Social Performance ...... 92

Figure 33: Normalized Economic, Environmental, and Social Performance of Non-Dominated

Solutions ...... 93

Figure 34: Solution A Building Types and Heights ...... 96

Figure 35: Solution B Building Types and Heights ...... 96

Figure 36: Solution C Building Types and Heights ...... 97

Figure 37: View of Scenario A from south along Wells/Wentworth Connector ...... 98

Figure 38: View of Scenario A from northwest along the Chicago River ...... 99

Figure 39: Optimization Solutions on the Planner's Triangle ...... 101

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Table 1: Summary of Design Variables ...... 36

Table 2: Summary of Buildings and Regression Date for Residential Rental Model ...... 43

Table 3: Summary of Building and Regression Data for Residential For-Sale Model ...... 45

Table 4: Single-Objective Genetic Algorithm Settings ...... 71

Table 5: Multi-Objective Genetic Algorithm Settings ...... 71

Table 6: Population Information for First Rank Solutions ...... 94

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CHAPTER 1: PROBLEM STATEMENT

1.1 INTRODUCTION

The great structural engineer Fazlur Kahn, father of some of Chicago’s most noteworthy landmarks, spent his career grappling with the notion of trade-offs. As demand for tall buildings exceeded what could be easily produced with a standard steel frame, Kahn realized tall buildings would have what he referred to as a “premium for height.” (Mufti and Bakht 2011).

Due to the shift from gravity-controlled design to laterally controlled designs, the taller a building was to get, the more robust its structure would have to be per unit floor area.

Kahn’s career focused on the development of schemes to solve this problem. While the clearest steel structures were cheap and economical, they were often limited to twenty stories in height. While inclusion of shear walls allowed for greater height, it did not totally obviate height restrictions and often left buildings with areas of undesirable opacity, anathema to an era when clear, trabeated solutions were in vogue. Framed-tube buildings could be built much taller, but only if their exterior columns were spaced densely together, limiting access to views and natural light. Tube-in-tube buildings could be constructed higher still, but in addition to the problems of the framed-tube, they also required an interior core directly at the center of the floor plate. “Braced-tubes” added strength, but often left individual apartments or offices staring at a large diagonal brace rather than a pristine view. “Bundled tube” buildings could be building the tallest of all, but their plan organization was broken into several smaller “spans,” with interior columns frequently interfering with interior program.

Designers will recognize in the above example a fundamental (if anecdotal) truth: that design is governed by a process of trade-offs whereby improving one metric of a design often involves limiting another aspect. This condition is not particular to buildings. In a 1991 article about engineering design, authors Otto and Antonsson note:

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“Design trade-off strategies are always present in the design process… ...[A] designer

may wish to slightly reduce some of the weaker goals in a design if large gains can be

made in the other goals, which would more than compensate for the slight loss. For

example, in the design of a sports car, the designer might reduce the safety margin of

some variables (like stress) to gain performance in other variables (like horsepower),

even though the stress may already be quite high.” (Otto and Antonsson, 1991).

1.2 THE PLANNER’S TRIANGLE

Considering specifically the built environment, one useful framework for understanding some of the most important trade-offs governing the building sciences is offered by the

Planner’s Triangle (Campbell 1996) which suggests a balance between nodes of social sustainability, environmental sustainability, and economic sustainability. While others had touched on this multi-variate approach to solution evaluation, most notably Elkington with his notion of the “triple bottom line,” (Elkington 1997), Campbell’s framework is unique it that it identifies the three nodes of the triangle as being perpetual conflict with one another.

The first conflict identified, the “Property Conflict,” describes situations in which social equity is placed at odds with economic growth. Campbell notes that this conflict is particularly complex because “each side not only resists the other, but also needs the other for its survival.” (Ibid.) One may call to memory recent debates regarding the merits of gentrification as a possible example of this conflict in architecture. The second axis of the triangle, the

“Resource Conflict,” explains situations in which economic growth clashes with environmental sustainability. Campbell characterizes this conflict through the lens of resources, describing

“tension between their economic utility in industrial society and their ecological utility in the natural environment.” However, observers in architecture may be more likely to recognize this conflict in the constant pressure to sacrifice long-term environmental performance for short-

2 term economic incentive. Finally, Campbell describes the third axis of the triangle in which social outcomes and environmental outcomes are at odds, which he calls the “Development

Conflict.” While Campbell points to “miners, lumberjacks, and mill workers” as examples of people who “see a grim link between environmental preservation and poverty,” this conflict is perhaps the most difficult to understand in an architectural context. Still, little imagination is required to envision a situation in which forest preserve must be left pristine or be torn asunder for the construction of affordable housing.

Figure 1: Planners' Triangle, example from Campbell and Fainsten (2003)

If one accepts, then, that trade-offs are intrinsic to the design process, and that

Campbells model, shown in Figure 1, is a useful formwork of characterizing these trade-offs, one is quickly leads to further questions regarding the present condition of these trade-offs, how these competing variables might be quantified, and how architectural outcomes can be improved through this process. This thesis will attempt to quantify select trade-offs for problems at a specific design scale. First, it will examine the present manner in which these competing objectives are being managed (or failing to be managed, as the case may be).

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Second, it will propose a method by which these trade-offs may be better understood and their outcomes optimized. Third, it will identify an appropriate scale and select a problem to which this new method may be most efficaciously applied and attempt to implement the method for these problems. Finally, it will evaluate the results of this limited implementation, drawing insights as to how the method may be more broadly applied.

1.3 THE PRESENT METHOD

Even in the absence of an empirically valid method of quantifying the trade-offs in the design process, construction in the building environment has (unsurprisingly) continued to advance. The planners’ triangle will likely have a ring of truth to practicing architects, who are in the business of deftly navigating the give-and-take of the design process. However, in the absence of a clear way to quantify this give-and-take, there is compelling evidence that the economic/environmental/social sustainability model is not presently at equilibrium.

Architects, being a heterogenous group, would likely value each node of the triangle differently if given the choice. However, by examining the built environment collectively and seeking to understand recent trends in the profession, it may be possible to draw some broad- strokes conclusions about the present state of the trade-offs in design. What follows is a brief assessment of how architecture, has accounted for each point of the triangle, culminating in an argument that too much emphasis has been placed on economic prosperity without sufficient consideration of the trade-offs implicit in this decision.

1.4 ARCHITECTURE AND THE ECONOMIC NODE

Seeking to quantify the economic fortunes of building as a discipline is a difficult task even without trying to parse the economic roll of the architect from the noise. It is possible, to some degree, to measure the fortunes of architects themselves as a group (although even this exercise is often reliant on self-reported data) and individually. However, there is little evidence

4 that the economic prosperity of architects is correlated with the importance each puts on the economic trade-offs of design. A more reasonable task may be to look at the prosperity of the built environment itself, as well as what architects are doing as a result of the present course of this prosperity and draw conclusions for this point.

It is possible to quantify the profitability of certain types of buildings to some degree. The MSCI

World Real Estate Index, for example, offers a “free float-adjusted market capitalization index that consists of large and mid-cap equity across 23 Developed Market countries.” (MSCI 2018).

More specifically, the MSCI World Real Estate Management and Development Index zooms more narrowly, focusing from the real estate sector at large to companies “engaged in real estate development and sales, real estate operations and management, or in real estate services.” In both cases, the general prosperity of the sector in the evaluated countries appears to be on the rise. The former cumulative performance index, which uses February

2003 as its basis (i.e. a value of 100), now reflects (as of February 2018) a value of 422.12, near all-time highs. The latter cumulative performance index, which uses April 2006 as its basis (e.g. a value of 100), now reflects a value of 170.95. One-year returns on the former index are positive at 3.95%, but appear to lag slightly behind the growth experiences since 1994, which is annualized at 6.71%. One-year returns in the latter index are much more strongly positive, with a rate of 20.01% vs. an annualized value of 4.63% since April of 2006. The picture here is somewhat mixed, yet the upward trend, subtle in some places and more obscure in others, is undeniable.

Another data point is provided by evaluating capitalization rates, essentially the ratio between the value of commercial realestate and it’s net operating income. Low capitalization rates indicate buys are willing to pay more for a given property than high capitalization rates, given static cash flows. Cushman & Wakefield’s mid-2017 survey of capitalization rates

5 indicate that they are relatively flat, with mild growth. More noteworthy in the report is that relatively constant capitalization rates are being experienced despite addition of square footage. The report notes that, since 2015, the “supply pipeline has markedly increased.” New construction, only 2.0% of inventory in 2015, has risen sharply to 2.9% of inventory in 2017

(Cushman & Wakefield, 2017). This suggests that while new construction is abundant, demand and prices for real estate remain high in the private sector.

A third data point may be the AIA’s Architectural Billing Index, seen as a bellwether of performance for the AEC sector. The January 2018 report shows Billings presently stand at

54.7 (indexed against a value of 50.0 for January of 2017). Inquiries and Design Contracts are also up slightly from the year before. While these metrics evaluate performance on a year over year basis, it is worth noting their continued rise, considering the economic strength experienced in 2017 (AIA, 2018). A broader picture view can be provided by evaluating the billing index on a three-year moving average. This method shows that even by 2014, early in the present building boom, the ABI has rebounded from values near 35 during the 2008

Recession to values near 50 (AIA, 2014). Generally, demand for architect’s services, correlated closely with building demand, appear to be high and rising.

A final data point may be offered by the cost of buildings incurred by consumers. It would stand to reason that if consumers are paying more for buildings, constructing said buildings is likely more profitable. Evidence for strength in consumer spending can be found in the S&P Case Shiller U.S. National Home Price Index. This index reports a value 197.01 for

February 2018, up from a post-recession low of 133.99 (FRED, 2018). The Consumer Price

Index’s Housing Component also shows strong upward movement, with an April 2018 value of

123.11, up from pre-recession lows which frequency sank below 100. At the consumer level, demand for buildings appears high, with the price consumers pay for shelter on the rise.

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While none of these metrics offer perfect proof that building is becoming more profitable for developers and other stakeholders, there does seem to be compelling evidence that the

Building Industry has become much more prosperous in recent years and, in some cases, months. This jibes intuitively with popular discourse on the economy and the increasing number of tower cranes visible each day in America’s urban cores.

Whether this prosperity has any connection, causal or otherwise, to architect’s valuing of economic principles over those of environmental and social good is not totally clear.

However, amongst the critical literature in architecture, there is certainly qualitative support for the notion of architects having become more concerned with economics than with other metrics of success. One incisive criticism of the profession on these grounds is offered by Paul

Knox:

“A starkly neoliberal political economy, in which progressive notions of the public

interest and civil society have been eclipsed by the bottom line in corporate and public-

private investment, means that design solutions have to be commercially attractive.

Compromises have to be made, projects have to be hustled, ideas have to be sold, and

unpalatable truths have to be spun into palatable propaganda. Planning and urban

design, deflected from issues involving regulation or social expenditure, were

increasingly pushed toward contributing to artful fragments of upscale suburbia as a

means of sustaining professional identity and credibility” (Knox, 2010)

Knox, not one to mince words on this particular topic, goes on to argue that while modernist designers strived for progress, postmodern architecture has instead sought

“consumption, hedonism, and the generation of profit, with little regard for the social consequences.”

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Another such indictment, of sorts, can be found in Klingmann, who argues in her 2014 book, Brandscapes, that the design of public space has shifted from the more egalitarian aims of the earlier 20th century to a more privatized experienced, controlled by large-scale corporate interests:

“As the symbolic definition and appropriate of spaces is increasingly reliant on

attributes that communicate “what space may be used and by whom,” the question

arises as to what extent social diversity is wanted or encouraged. The accompanying

dangers of exclusion, and of the privatization of city live, have been noted by many

critics…” (Klingmann, 2014)

Certainly, these types of theoretical criticism would appear to indicate that at least some amongst the profession believe that the trade-off between profitability and the other goals of architecture (among them social and environmental sustainability) have not been properly accounted for.

Evidence that the names and likeness of architects are being co-opted for economic purposes is certainly not difficult to come-by, if anecdotal in nature. Consider the residential high-rise located at 8 Spruce Street in New York. Originally known by its address, the building was re-branded as “New York by Gehry,” in reference to Frank Gehry, the projects architect.

The Projects website, which refers to Gehry as “one of the most celebrated architects practicing in the world today,” also notes that “master architect Frank Gehry has reimagined the classic Manhattan high-rise,” and that “Gehry’s distinctive aesthetics is also reflected in the residences and three floors of amenity spaces, all designed with custom furnishings and installations.” While the so-called High Modernists, forged in the egalitarian fires of

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Weissenhof, would eventually design building for IBM and Pan-American Airlines, one finds it difficult to see Mies van der Rohe conceiving of the idea of referring to 860-880 Lakeshore Drive as “Chicago by Mies.”

Clearly, architects in today’s world are incentivized to focus on profitability. This is not necessarily problematic, in and of itself. A profitable building is not necessarily a good or bad building, or a sustainable or un-sustainable building. If architects continue to focus on the

Social and Environmental poles of design, and consider these elements in balance, profitability becomes one of many competing goals vying for the attention of the architect. However, as the following pages will show, it appears as though far less progress has been made on social and environmental goals as on economic goals.

1.5 ARCHITECTURE AND ENVIRONMENT

In contrast to the world of economics, where the built environment appears to be performing well, the complicated relationship between architects and environmentalism appears to be offering less optimal outcomes. While a focus on economic sustainability is clearly present, environmental sustainability appears to be insufficiently accounted for in the trade-offs of design.

To the credit of some, select designers can be seen among the vanguard of the modern environmental movement. Sustainability in design professions can be seen at least as early as

1969 with the work of Ian McHarg. McHarg, and his opus, Design with Nature, take their place among the early turning points of the environmental movement, falling between Silent Spring

(Carson, 1962) and the 1973 Arab Oil Embargo. Within the realm of architecture specifically, early adopters like Hassan Fathy and Laurie Baker were among the first to recognize and revive the sustainability of indigenous forms, often as a matter of necessity. By the mid-1990’s, the notion of architectural sustainability was receiving widespread publication in the works of Ken

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Yeang (Yeang, 1995). By the end of the century, the Congress for New Urbanism has elevated some ideas regarding sustainability to the urban scale (Duany et al, 1999), although these came mixed with the odd, retrograde language of post-modernity.

To a viewer watching the contemporaneous literature as the sustainability movement evolved, it may seem as though architects and designers were among early adopters and remain some of the most ardent defenders of the environmental performance of the built environment. For some designers, this may very well be true. It is certainly not a difficult task to find a building advertising it’s architecture as sustainable. However, it is additionally clear that while sustainability is a driving force for some, others, perhaps even a majority of others, remain uncommitted to its principles. Further, it is evident that some projects advertised as ecologically sensitive are, at best, of uncertain ecological merit.

While sustainability may be common in so-called “high design,” a drive down any suburban street will showcase numerous buildings that appear to be designed without considering ecology (and likely some that appear to have not been designed at all). The split between the sort of practice that can indulge in the luxury of environmental performance and the sort of practice which dictates how most of the world looks is an industry-wide phenomenon. Klingmann notes the division in practices:

To this day, two types of architectural practices continue to split architectural production

– commercial practices – which pride themselves on being consumer friendly and which

for the most part offer conservative solutions that are based on tried and true formulas –

and critical practices, which offer innovate design but attract few customers. This

enforced schizophrenia between business and cultural ambitions, commercial

objectives and ideological pursuits, is hardly a desirable condition to maintain.

(Klingmann, 2014).

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Berardi, writing about the profession at large, offers a similar observation:

The process of specialization was pushed to the extremes, the common ground of

intellectual exchange-which was the public sphere of the social movements-was erased.

Everybody is busy working in conditions of isolation and competition; engineers and

poets belong to two distant dimensions that today never meet.”

Knox connects this disconnect to the foundations of architectural education:

“Architecture schools, in particular, tend to have a fraught relationship with the

profession, resenting the restrictions on curricula imposed by professional accrediting

agencies and eschewing the issues that grads will face in the transdisciplinary and

commercially oriented practices of medium- and large-sized firms (that represent about

80 percent or more of the profession) in favour of idealized notions practice (sic) in

small, boutique firms committed to ‘good’ design.” (Knox, 2010)

Even those firms which consider themselves among the environmentalist vanguard can often deliver projects which are marketed or justified as “sustainable,” but in reality, offer no particular improvement on the performance of buildings which do not adapt this mantle. Even the United States Green Building Council, arbiter of the much-publicized LEED Program, must acknowledge that buildings awarded a plaque for sustainability are not necessarily performative. While a survey of 121 LEED Buildings found that the energy use intensity for those buildings was 24% below that the of the total building stock (69 kBtu/sf vs. 91 kBtu/sf), the basis for comparison was the total commercial building stock, representing not just new

11 construction buildings, but also leaky and inefficient buildings built decades ago. (Turner and

Frankel, 2008). Fully one quarter of buildings certified by LEED failed to achieve an Energy

Star rating of 50 (making them worse performatively than a median building of comparable use). (Ibid.) Further, while LEED Buildings have been found to use roughly 28% less energy than the code baseline, these buildings are split about equally between buildings which used more than anticipated and buildings which use less than anticipated. At least 10% of LEED buildings sampled, included six Gold/Platinum rated buildings, actually performed worse than the energy baseline. (Ibid.) Another survey of 100 LEED buildings yielded similar results: while LEED buildings used, on-average, 18-39% less energy per floor area, 28-35% of LEED buildings actually used more energy than the comparative standard. (Newsham et al 2009)

In the case of LEED, architects or other design professions have deliberately chosen to meet the onerous reporting requirements to receive laudation for their building performance.

Yet, even in these buildings, performance is far from guaranteed. Meanwhile, the have-nots of the architectural practice world, miles from LEED certification, rely mostly on Energy Codes to regulate the performance of their buildings. Fortunately, the International Energy Conservation

Code and ASHRAE Standard 90.1 continue to impart greater and greater requirements on building assemblies, operations, and performance. While the forefront of architecture may make broad overtures to the aesthetics of sustainability, architects have largely ceded the regulatory apparatus to other design professionals. There are a frustrating number of architects standing ready to build the worst building allowable by code.

All of this, of course, expresses only concern for operational energy. A true analysis of

Building Lifecycle Energy consumption remains elusive. Sartori and Hestnes (2007), review several articles on the calculation of embodied energy and it’s relation to operational energy, finding that various sources quote embodied energy as making up anywhere between 5 and

32% of total Lifecycle Energy. Buildings with lower operational energy were found to generally

12 have higher embodied energy, although additional embodied energy was almost always worthwhile if it resulted in substantial reduction of operational energy. Drew et al (2015) expound on this idea even further, arguing that to be truly complete, transportation would energy associated with building modalities would have to be evaluated contemporaneously with the buildings themselves.

While LEED and some other architect-friend tools do make nods to lifecycle energy costs (e.g. LEED credits for Sustainable Sites and Local Materials,) the prescriptive method used in codes relied upon by many architects often specify only material properties (e.g. wall thermal resistance) or, at best, energy use intensity per unit floor area. These metrics, the most common in use, miss the place of buildings in in its larger context from an energy perspective.

Perhaps the most compelling evidence that environmental factors have become subservient to economic factors is offered by the very world around us. US Commercial

Buildings built between 1990 and 2012 use an average of 80.9 kBtu/sf of floor area. While this is a marginal improvement from the 84.5 kBtu/sf used by buildings built between 1960 and

1989, it actually represents worse performance than buildings built in 1959 or before, which had average EUI’s of only 68.6 kBtu/sf (EIA, 2012). While this method fails to account for improvement in thermal comfort standards or continuous ventilation requirements, it remains damning that brand new buildings, for all their standards, actually use more energy than older ones.

Residential Buildings fare little better. The median and average size of new single- family homes, which took a brief respite from its meteoric rise during the Great Recessions, has now resumed its sharp upward trajectory (Census, 2010). 23.1 million housing units, 19.54% of all those in the United States, are presently over 3,000 square feet in gross floor area. These changes are occurring at a time in which average household size is shrinking. In 2015, the average home used 30.3 million BTU per household member per year. This certainly

13 represents an improvement from 1993, when this measure was first calculated, when the average home used 40 million BTU per household member. What’s more, much of this gain has been recent, with values of 34.9 million BTU per household member being published as recently as 2009. However, these gains have been largely offset by the growth in population.

In 1993, all US homes used 10.01 quadrillion BTU’s. By 2009, that number had grown to

10.183 quadrillion BTU. It wasn’t until 2015 that a substantial decline, to 9.114 quadrillion

BTU’s was observed (EIA, 2015). While residential buildings were becoming more performative, the energy savings of these performance enhancements were, to some degree, offset by a demand for more units and units of greater size.

The evidence for the growing profitability of buildings certainly seems more convincing that the evidence for growing environmental performance, where gains have been modest. To what degree the architect bears responsibility for the valuation of economy over environment is unclear. A compelling argument can be made that architects, always serving at the pleasure of their clients, are creating the most performative buildings allowed within the framework of a growing demand for economic return.

Of course, perhaps a more convincing argument can be made that architect’s ultimate responsibility is to the health, safety, and wellbeing of the general public, and not exclusively to their clients. Operating under this assumption, it can be easy to see why architects can be seen as at best complicit and at worst willing participants in placing building economics ahead of environmental performance.

1.6 ARCHITECTURE AND SOCIAL JUSTICE

So far, a survey in the present methods of architecture has indicated that architect’s focus on economic performance appears to have been more successful than focus (or the absence thereof) on environmental performance. While evidence of economic prosperity was

14 strong, evidence of environmental responsibility was, at best, mixed. In considering the third node of the triangle, equity, the evidence would seem to indicate that this node has more in common with the mixed environmental results than the more uniformly positive financial results.

Certainly, the present climate of economic inequality cannot be blamed entirely on architects, or even on the built environment. A full investigation of the causes of growing inequality are outside of the scope of this research. However, it would be sufficient to say that economic, demographic, and political components confound serious pursuit of how the built environmental impacts equity and social justice. However, it may be possible to examine some built-environment-specific criteria to see to what degree equity is valued by architects.

It will certainly come as no surprise to those who have watched rise of neo-liberalism as a political doctrine that privatization has played an increasing role in our world, and this has manifest itself in our built environment. In 1996 Privtopia, Evan McKenzie lays out a compelling argument for the gradual shift from public land use policy to private control of land through the rise of instruments such as restrictive covenants, homeowners’ associations, private security and infrastructure, and even physical barriers (e.g. gated communities). For “public” spaces, where physical gates and doormen are seen as less acceptable, private interests has found ways to keep control out of public hands, as argued by Klingmann (2014) in her evaluation of

Celebration, Florida:

“The subtle control of communicable space takes on a strategic significance here.

While the proverbial gates have been replaced by a twenty-four-hour security patrol to

create private, branded space that pretends to be public, Celebration is more than just

an example of highly controlled urban environment – it is also an exercise in moral

regulation.”

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While the rise of privatization of built spaces may represent a threat to equity in the developed world, there can be little doubt that more serious affronts are found in the developing world. An example of this can be found in the construction associated with the

World Cup being held in Qatar. The late Zaha Hadid, Pritzker prize winner and star-chitect, was tasked with designing the signature al-Wakrah Stadium, one of the centerpieces of the event.

When evidence emerged that significant human rights violations were occurring, including the harm and even death of workers building that stadium, Hadid told the Guardian Newspaper shortly before her death that “I have nothing to do with the workers.” When further pressed about the 800 reported deaths, Hadid reaffirmed that “I cannot do anything about because I have no power to do anything about it. I think it’s a problem everywhere in this world.” (Wilson et al. in Deamer, 2015)

Architects, for the time being, seem content to throw up their collective arms when confronted with a growingly less just world, adding that they are only acting as the agents of others. Perhaps the conflicts faced by architects are best summarized by Worthington (2009), who writes:

“Today the profession continues to be faced with these paradoxes, and there is

uncertainty about whether its prime role is social concern for the wider interest of

society and the user, or whether it is primarily concerned with the particular interests of

its developer client or success of the practice, now most probably a limited company

whose economic livelihood may rest on the next job from a contractor. The

architectural profession feels alienated. The professions are often no longer head of the

team, being increasingly rejected by the client and user and driven by commercial

imperative.”

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While architects are licensed ostensibly to protect the “health, safety, and wellbeing” of the general public, it is clear that architects are either impotent or indifferent to the requirements of this task. Perhaps this is a result of the changing political and economic winds. Perhaps it is because that in a post-Pruitt Igoe era, Architect’s no longer feel like the moral authority in ensuring social equity. Certainly, twentieth century policies of “slum clearance” and the construction of isolated workforce housing had adverse effects from which the social cohort of the profession is still reeling.

All of this is not to say that today’s architects are unable to conceive of socially just and healthful spaces when given the means and reason to do so. Another Pritzker-prize winning architect, Alejandro Aravena has offered a promising example of what a more equitable architecture might look like through his incremental housing projects in Chile. In these subsidized projects, funds are saved by fitting out only one half of a house, leaving the other to be developed at the discretion of those that dwell within. This affords opportunities for family growth, small, local businesses, and ultra-fine mixed-use solutions (Aravena, 2011). In this case, architecture seems to be designed explicitly to help those transitioning from poverty to better circumstances.

Still more promising work is being done in the research-based corners of the profession. Sarah Williams Goldhagen’s Welcome to your World has summarized some of the excellent work being done on the interaction between the built environments and the quality of life of those who live within it. Still others have focused on the effects of architecture (Ulrich,

2006) and Landscape Architecture (Brown and Corry, 2011), in improving healthcare outcomes. Yet, to date, there remains no overarching means of balancing the newly understood benefits of certain architectural strategies with the other requirements of these buildings. It may be, as research has suggested, that patients with a view of green spaces

17 have a shorter length of stay. However, balancing this wellbeing component with the cost of glazing and the environmental performance of façade penetrations is not well understood.

Clearly, the picture for architecture as a progenitor of social good is, at best, mixed.

While certain buildings and research appear to have demonstrated an interest in social justice and health, safety, and well-being, in other cases, prominent architects have washed their hands of something as basic as the working conditions of those doing the actual building itself.

1.7 THE LACK OF PRESENT BALANCE BETWEEN NODES

When considering the present methods by which architects the trade-offs implicit to the design process using the Planner’s Triangle, it appears as though, from a sustainability standpoint, the design professions have fallen out of balance (see Figure 2).

Figure 2: Trade-off's Out of Balance. Adapted from the Planners' Triangle

Of course, as previously discussed, the degree to which this shift may have occurred is nearly impossible to measure. While a review of the qualitative and semi-objective measures presented above appears to indicate a profession more concerned with profit that the other nodes of the triangle, at present, there is no reasonable method to quantify these trade-offs for projects with any degree of scale. Architecture is also an exceptionally wide discipline. While

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Figure 2 shows a single point which has drifted in a single direction, a more accurate, if less legible, diagram would likely show a cloud of points which has drifted disparately from the center, with a possible higher density of points towards the economic node.

At present, there exists no meaningful method by which to evaluate exactly how a given practice or, more to the point, a given architectural solution has shifted or to what degree it succeeds or fails in balancing the competing priorities of sustainable design. With the flaws of the present method clear, in the following section, a new methodological framework for just such evaluation will be explored.

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CHAPTER 2: LITERATURE REVIEW

2.1 OPTIMIZATION

In the previous section, a problem of prioritization was identified. Specifically, evidence suggested the absence of an empirically rigorous way to quantify the design trade-offs between environmental, economic, and social sustainability. The notion of the competition between several competing and contradictory variables, however, is not unique to architecture.

At is root, such a problem is a question of multi-variate optimization. That is, in order to develop a method of addressing the problem identified infra, one must first seek to understand methods of optimizing. This chapter explores one such method, the genomic algorithm, and its potential to address such problems.

2.2 THE GENETIC ALGORITHM

Haupt (2004) defines the Genetic Algorithm as:

“…an optimization and search technique based on the principles of genetics and

natural selection. A GA allows a population composed of many individuals to evolve

under specified section rules to a state that maximizes the “fitness” (i.e. minimizes the

cost function).

Put simply, a minimum seeking algorithm in which a randomly generated sample of “solutions” to a given problem (in our case, the configuration of a series of buildings) are evaluated by a cost function (in our case, their social, economic, and environmental impacts), and the most

“fit” solutions are the refined through a series of “generations.” The process mirrors Natural

Selection in that in occurs “through repetitive application of the mutation crossover, inversion, and selection operators.” (Gupta, 2012)

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The exact invention of this process is not totally clear. Evolution-based computer simulations occurred as early as 1954 in the works of Norwegian-Italian mathematician Nils Aall Barricelli

(Gupta, 2012). Australian quantitative geneticist Alex Fraser later, in 1957, published a series of more widely circulated papers on the topic (Ibid.) Fraser, collaborating with Burnell published a book on the topic in 1970, with further work done in the intervening decade by Crosby in

1973. Other pioneers include Hans-Joachim Bremmermann, Richard Friedberg, George

Friedman, and Michael Conrad (Ibid.) Where exactly these methods transformed into a modern GA is not totally clear.

Haupt (2004) dates the creation of the modern genetic algorithm to John Holland in

1975 and the publication of his seminal book Adaptation in Natural and Artificial Systems.

Holland’s student, David Goldberg, used this method to solve a difficult problem involving gas pipeline control and transmission in his 1989 thesis.

According to Gupta (2012), a genetic algorithm, in its most basic form, requires a genetic representation of the solution domain (i.e. all possible solutions) and a fitness function to evaluate the solution domain (i.e. some numerical method of assigning a value for performance to each solution). Of course, not all problems have solutions which are best evaluated on a single cost function. It is certainly possible to have a problem in which optimization on single objective is paramount, e.g. finding the lowest amount of time in which a task can be completed. It is, however, often the case that solutions are evaluated on myriad of factors. Instead of merely knowing which solution is the fastest, you may need to know which solution is the fasted and least expensive, or fastest, least expensive, and more efficient.

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2.3 ADDRESSING MULTIPLE OBJECTIVES AND THE PARETO SURFACE

The simplest way in which to conduct multi-variate genetic optimization (i.e. with multiple cost functions) is simply to weight each function according to its importance.

Problems often arise, however, when comparison occurs across multiple objectives which cannot be simply weighted. As Kramer (2017) notes:

Without a decision for a weight of the objections, it is difficult to solve the optimization

problem. As no unambiguous comparison between solutions that are optimal

concerning at least one objective is possible, solutions of that kind are incomparable.

However, solutions that are worse in all objections are outperformed and from this

perspective useless. However, the challenge in multi-objective optimization is to

approximate a set of solutions that constitute a compromise between all objectives.

This set is also known as the Pareto-set.

Solutions in which the cost function for one objective can be improved without deteriorating the performance of the solution on a different objective’s cost function are said to be “dominated” (Ibid). Ergo, for problems with continuous variables and multiple objective criteria, there are infinite possible set of solutions which together form the “Pareto Front”

(Haupt 2007). Generally, using the sum of weighted cost functions is impractical for finding the

Pareto front, as the necessitates running many possible cost functions weights and is often computationally infeasible (Ibid.)

Fortunately, there are more sophisticated methods. These include Schaffer’s vector evaluated GA, devised in 1984 (Ibid), Fronesca and Flemming’s multi-objective GA, devised in

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1993, and non-dominated sorting GA’s of the first and second type (Ibid). Fortunately, the wide applicability of these methods has, in recent years, lead to their inclusion in a variety of software packages.

A visualization of the Pareto-front within a solution space can be found on the left of

Figure 3. As this illustration represents only two cost functions, f1 and f2, it is linear. As shown in the right side of this figure, for a three-objective optimization, the Pareto front will actually consist of a surface.

Figure 3: Pareto Front (Left) and Surface (Right) Image adapted from the University of Sheffield https://www.sheffield.ac.uk/acse/staff/peter_fleming/intromo

2.4 OPTIMIZATION IN BUILDINGS AND ARCHITECTURE

While genetic algorithms, both single-objective and multi-objective, can be applied to a wide range of optimization problems, at issue here is their application towards architecture (in the bricks and mortar sense of the word). Fortunately, a wide variety of architectural problems have been explored with the GA to date.

Perhaps most common is the use of GA in optimizing energy performance. This may because energy performance, being among the quantities metrics on which architecture is evaluated, lends itself to the construction of straightforward cost functions. The simplest form

23 of environmental modeling is the single-objective variety, in which a single cost function (e.g. energy use intensity) is evaluated for given variables. Congradac and Kulic (2009), for example, use variables such as damper position, blower speed, and output temperatures to optimize CO2 concentration control for the reduction in energy cost. Similarly, Kolokotsa

(2002) uses smart cards to evaluate user-reported human thermal comfort as a cost function by which various parameters of an existing HVAC systems are optimized. Ooka and Komamura

(2009) examine a Tokyo hospital over a 24-hour period and optimize single-day energy performance for variables of equipment size and configuration. In these cases, objectives vary between minimizing energy use and minimizing thermal discomfort, but variables (the operational parameters of an existing or theoretical HVAC system) are very similar.

In most cases, evaluation on an energy-related variable involves the integration of an existing hourly energy simulation program. Alajami and Wright (2014) explored this connection and identified the most important control parameters in single-variable configurations.

More complicated versions of energy optimization often try to account for cost. Znouda et al (2009) conduct separate GA optimizations for environmental performance and cost of a small structure in a Mediterranean climate. Variables in this example include window size and façade orientation. However, this method is limited, as it uses two single-objective GA’s, and is therefore incapable of considering multiple objectives and the trade-offs therefrom simultaneously. Li (2017) surveys recent publications and reports that 68.4% of genetic optimization strategies in published building optimization cases are of a single-objective type, either basic or improved.

Others solve this issue through the use of multi-objective evaluation criteria in finding a

Pareto-optimal solution set for an architectural energy problem. Wright et al (2002), for example, use a multi-objective GA with the dual objectives of operating cost and human thermal comfort and variables including fan speed, volume flow, and air and water velocity

24 within the HVAC system. Similarly, Caldas and Norford (2003) explore the use of a non- dominated sorting GA to optimize for the multiple criteria. The first optimize simultaneously for first cost and operational cost of equipment, then for lighting and heating energy used, considering primarily geometric variables in their effort.

Asadi et al (2014) use energy, occupant comfort, and retrofit costs in a school retrofit context. Ascione et al (2015) focus more narrowly, evaluating only façade performance in a

Mediterreaan context. Like Asadi et al, they use minimization of energy and maximization of thermal comfort as their dual objectives, electing not to consider cost. Yu et al (2014) also consider energy use and occupant comfort, this time for a case of a typical building in China.

Chen and Yang (2017) opts to consider different types of energy, in this case, lighting, cooling, and heating energy demand, as multiple variants in a high-rise design context.

These latter, multi-objective GA’s demonstrate the capability to find a Pareto-optimal set of solutions to complex problems. However, their considering is often limited to a given building or system, and each limit itself to only two (or in Asadi’s case, three) objectives at a time. Each is similarly limited by having only energy and related variables (e.g. cost of equipment, occupant thermal comfort) as cost criteria, perhaps because these can be measured objectively more easily.

In addition to energy, it bears mentioning that structural performance has also been considered through genetic optimization. Pourzeynali et al (2006) demonstrate that a fuzzy logic controller combined with a genetic algorithm can be used to control an active tuned mass damper successfully. Wu and Katta (2009) evaluate how best to reduce the surface to volume ratio of given shapes, with the applied solution of reducing the total amount of material needed for structurally viable stadium roof construction. Deutsch (2017) catalogs the use of GA’s by practitioner Robert Vierlinger, who has used optimization to minimize the structural material

25 necessary for a space-frame-type system and also for a two-dimensional truss given a static loading condition.

In the case of energy performance and structural performance examples enumerated above, a wide variety of variables and objectives have been considered, mostly for objective cost functions which can be more-or-less straightforward in their aims. However, even though many of these solutions are nominally multi-objective genomic algorithms, they often fail to consider more than one area of building performance. Some consider cost and structural integrity, or cost and environmental performance, but rarely do they cross outside of their discipline. For example, none of the above examples consider both structural and environmental performance simultaneously. Additionally, all limit themselves to two variables, preferring the comfort of a two-dimensional solution space and a Pareto-frontier to a three- dimensional solution space with a Pareto surface.

There have been gestures towards more comprehensive ways of thinking that work across disciplines. Wang (2004) evaluates both life-cycle cost and life-cycle energy performance for variables including construction type, aspect ratio, and window-wall ratio. This example simultaneously considers first costs, operating costs, initial energy performance, and life cycle performance, expanding the complexity of the model. While only two objectives are used (lifecycle cost and lifecycle energy performance) the complexity of these two cost functions hints at the more complex nature of the problem at hand. Wang (2005) even expands this work through the design of a building, however, it is a relatively simple single- story office building. More recently, Karaguzel et al (2013) consider both lifecycle energy cost and typical annual energy uses. While this hints at the possibility for life-cycle and cost-based evaluation, it fails to account for building first cost and is applied only to a relatively simple office building.

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Lin and Gerber (2014) demonstrate an ability to use multi-variate GA’s as a more complex design tool, employing it earlier in the design process. Their algorithm uses three cost functions simultaneously, one each for net present value, energy use intensity, and measure of spatial efficiency which they call “spatial programming compliance score.” Here we see possibly the most complete gesture towards the complexity of trade-offs in building design.

Of course, genetic optimization is not the only route by which optimization can be undertaken with machine-learning methods. Delgram et al (2016) for example, use Particle

Swarm Optimization, an entirely different construct, for multi-objective building energy performance. Latief et al (2017) use questionnaires and a case study to arrive at conclusions regarding offsets between upfront cost, operational cost, and energy use. Papantoniou et al

(2014) use several methods simultaneously, combining some genetic algorithm elements with an artificial neural network and multi-step optimization. Yet, it appears as though the Genetic

Algorithm is the preferred and prevailing method of building optimization.

2.5 OPPORTUNITIES FOR EXPANSION

While the examples above certainly offer compelling evidence of the power of this method, they are not without their limitations. Two areas which demand further expansion are more inclusive and comprehensive evaluation criteria, and consideration of problems at a much greater physical scale.

To date, evaluation criteria have been many, however, it appears as though the simultaneous consideration of the three nodes of the planner’s triangle has yet to take place.

No example readily available in the literature considers economic, environmental, and social

27 sustainability simultaneously. Additionally, each node of the planner’s triangle has room for development in its own right:

 Economic performance has, to date, been measured through first cost,

operational/energy costs, and/or net present value. Yet, economic sustainability

entails more than just these factors. A comprehensive evaluation of economic

sustainability must include the total cost of building, the expected cash flow from rent

or sale of the space during its useful lifetime, and the cost of operations and

maintenance.

 Environmental performance has, to date, been measured through energy use intensity,

energy cost, and lifecycle carbon emissions associated with operational energy. Yet,

environmental sustainability entails more than just these factors. A comprehensive

evaluation of environmental sustainability must include embodied energy, operational

energy, and the energy associated with transportation necessary to support building in

a given way.

 Social Sustainability has, to date, not been evaluated rigorously using these

optimization methods. This is likely due to the difficulty in quantifying social goals. A

comprehensive evaluation of social sustainability must first solve the problem of

evaluating what variable effect this outcome and how an objective cost function can be

constructed.

Physical scale has also been fairly limited in evaluation through this method. The examples above work at a variety of scales, from the size of a single truss, to the size of a room, to the size of a large hospital building. Yet, increasingly, architects are asked to think about problems in the context of their larger urbanity.

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In the following section, with deals with methods and outcomes, this thesis will offer a more comprehensive view of how the method in question may be expanded to work at the urban scale and to consider the evaluation of buildings more holistically.

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CHAPTER 3: METHODS

3.1 EXPLORING A NEW METHOD

Chapter One argued that the architectural profession is sorely in need of new methods by which empirically rigorous design may be accomplished. In Chapter Two, the genetic algorithm and its application to architecture was considered as a mode of optimization for solving such multi-faceted problems empirically. Chapter three will lay out a case to which such an algorithm may be applied. This will be accomplished through finding an appropriate scale case for intervention and designing algorithmic parameters by which the case will be evaluated.

3.2 CASE DEFINITION

As shown in the previous section, modeled optimization problems in architecture have tended to focus on the building scale. In some cases, this was the scale of a large building

(e.g. a hospital) and in others it was a small, theoretical building given few attributes (e.g. single-story office). Also shown were examples of single component optimization (e.g. trusses). If one were to envision the built environment as a spectrum stretching from the detail level to more macro levels, as shown in Figure 4, one would likely consider these scales the purview of Building Information Modeling. As Deutsch (2017) points out, the software tools typically employed for optimization at this scale are quite different than the software tools used for other scales. Software employed at this scale include the Rhino-integrated Grasshopper and the Revit-integrated Dyanmo, which provide optimization capabilities within a BIM platform for detail to building scale problems (Ibid.).

At the other end of the spectrum, the sustainability of urban systems has been explored through the use of urban and regional-scale GIS systems. Deal and Schunk (2004) develop the Land Use Evolution and Impact Assessment Model to answer questions about

30 environmental sustainability and Land Use policy. Even “below” the urban scale, Steinitz

(2012) framework for Geodesign shows promise in improving urban performance as evaluated by many stakeholders using disparate criteria for large urban areas varying in size from university campuses to portions of modern megalopolises.

Figure 4: Levels of Digital Explorations in Architecture: Adapted from Carl Steinitz (2012)

Ergo, enhancing multi-objective urban performance is taking place at the scale of inches in grasshopper and at the scale of miles in ArchGIS. Yet, one cannot help but notice a gap but notice a gap between these two strategies, as Architecture seldom evaluated in inches and almost never quantified in miles.

Indeed, some of the most compelling development opportunities facing cities today live in the wide gap between civic-scale geodesign and building-scale BIM. This is the realm of neighborhood-scale optimization. A recent popular press article identified thirteen “mega- developments,” occurring in just a single city, Chicago, at just one nexus in time (Koziarz

2019). Some of these projects amount to a single building or a handful of towers (e.g. Old Post

Office Redevelopment, Wolf Point Tower Construction). Others appear more visionary, ultra- long-term proposals of civic transformation, involving multiple supertall buildings (e.g One

Central) or transformation of huge swathes of city (e.g. Former Southworks Site). Yet, several projects are proposed at the scale of several city blocks: sites to large to consider as a building, but too small to consider as an entire sector of city. These projects are as follows:

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Lincoln Yards, The River District, Southbank and Riverline, The 78, Southbridge, and The

Burnham Lakefront.

Figure 5: Six Proposed Mega-Developments in Chicago. Adapted from Google Earth and Photos by Respective Developers

These developments are typical of the large-scale urban infill sites on which mega- developments are often placed. Formerly, the sites were a Steel Industrial Facility, a

Newspaper Printing Plant, a Railroad Station, a Railyard, a Public Housing Project, and a

Hospital, respectively.

For the purpose of this investigation, consider The 78, a development proposed at the southwest corner of Clark Street and Roosevelt Road in Chicago. This site was selected from among its peers as it has a defined and publicly available program for the proposed $7.2 billion

32 dollar project on-site (Baiceanu 2019). This program includes a total of 13 million square feet of gross floor area (Ori 2019). The site is also relatively adjacent to Downtown Chicago, which will make finding economic comparisons to existing buildings easier. The 78 will provide the lab in which it will be possible to test the idea of multi-variate optimization for social, economic, and environmental sustainability at this scale.

In considering this site, the model will attempt to optimize a set of variables for three objectives (social, economic, and environmental sustainability) given a set of practical constraints. These components are to be configured as explained below.

3.3 VARIABLES

Given a site on which to operate, it is now possible to define which variables a model will consider. It is possible to imagine an infinite number of discrete and continuous variables associated with the construction of a development of such a massive scale. As the literature review has shown, just one truss in one building can provide fertile ground for optimization.

However, considering such small building components would be impractical for a neighborhood scale investigation. Instead, a more broad-strokes mix of program and height will be more appropriate for the scale in question.

The first variable will concern the number of buildings associated with any given use.

While the proposed project has, to some degree, an established amount of gross floor area for use, it is unclear how this balance was reached and if this balance is optimal for any given objective. Possible use types will include Apartment Rental Housing, Condominium For-Sale

Housing, Affordable Apartment Housing, General Office Space, and Hospitality Space. These five use types were selected as they seem to account for the vast majority of gross floor area in the type of high-rise development proposed for this site. While it is typical for high-rises to have some ancillary functions (e.g. Ground Floor Retail, Parking Podium), generally such buildings

33 are classified by their primary use as one of the five aforementioned use types. This variable will be discrete (as one cannot have a fraction of a building) and will be limited to values greater than zero in single increments of one. The maximum possible value for each type of building will be ten, as this allows for fifty possible buildings (five uses times zero to ten buildings per use) which would saturate the site. The maximum also accounts for the fact that demand would likely be reduced with each new building. Even if the algorithm were to select to build, say, forty hotels, this solution would not be appropriate as no market could support such an abundance of hospitality.

The second variable will concern the optimal height for each type of building. Once again, this variable will be discrete, as it will be measured in floors rather than in strict vertical distance, and one story of a building cannot be sub-divided. Once again, an upper and lower bound for this variable is appropriate. The lower bound considered will be ten stories, as buildings shorter than this are generally not appropriate for the urban character of the site. The upper bound will be 100 stories, as building above this height begins to strain credulity as to economic and structural ability.

Height alone may initially seem an insufficient parameter to adequately describe the scope of a building. Even a simple rectangular prism has three dimensions to be considered.

Yet, if one examines the common normative theory around each building type, one finds that certain floorplate depths and lengths are often common to building types. As Residential and

Hotel Uses require relatively narrow floor plates, a typical building width may be on the order of

60’-0”. This allows for two 30’-0” units to be arranged across a double loaded corridor. Such narrow floor plates allow the residential spaces access to natural light and affords suitable views of the surroundings. It is possible that an optimal width for such a building may be 29’-0” or 31’-0”, but determining the exact optimal in width is outside the scope of the present

34 investigation. The selected value allows for two or three individual spans of depths appropriate to residential and hotel spaces.

Width alone is insufficient to define a floor plate area. Even if given a height in stories, in order to define a floor plate area, one must also know the length of the building. For apartment and hotel buildings, a length of 200’-0” is used for this investigation. This round number was selected as it appears consistent with the typical high-rise residential or hotel floor area of approximately 12,000 square feet. If one looks at recently completed building stock, one finds the majority of such buildings have floor plates with similar dimensions to allow for economic construction in post-tensioned flat-plate concrete. Once again, determining if 11,000 square feet or 13,000 square feet would be more appropriate is outside the scope of the present investigation.

If Rental, Condominium, and Affordable Housing are similar to Hotels in that shallow floor plates are required, then it is also fair to note that in office buildings, generally much broader floor plates are required. Typical Class-A offices have much longer “leasable spans” than their residential counterparts. These spans tend to be column-free to allow for optimal interior organization of a given office tenant’s space. For an office floor plate, an exterior span is likely to be closer to 40’-0’. Similarly, these buildings are generally not organized around a single double-loaded corridor, but rather a central circulation core. Ergo, let us assume that the width of the building will consist of two 40’-0” lease spans to the exterior and a single 40’-0” span for the interior core, providing a total building width of 120’-0”. Generally, office floor plates are much larger, with buildings often being constructed of a composite structure consisting of a cast-in-place concrete core with hot-rolled steel beams and columns and composite decking. Given this structural system, one may assume a length of approximately

300’-0”, which creates a typical office high-rise footprint of 36,000 gross square feet.

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As we have defined typical floors for each building type, then finding the gross floor area of a given building is simply a matter of multiplying the number of stories variable by the set foot-print size to arrive at a given floor area. Ergo, the variables for our model will be limited to number of buildings of a given use, and optimal height for each use type. Each permutation of solution will the be evaluated by a set of three objectives: economic, social, and environmental. These objective functions are discussed in greater detail below. A summary of variable structure is shown in Table 1 below

Table 1: Summary of Design Variables

Variable Lower Bound Upper Bound Number of Market-Rate Apartment Buildings 0 Buildings 10 Buildings Number of Condominium Buildings 0 Buildings 10 Buildings Number of Affordable Housing Buildings 0 Buildings 10 Buildings Number of Offices 0 Buildings 10 Buildings Number of Hotels 0 Buildings 10 Buildings Height of Market-Rate Apartment Buildings 10 Floors 100 Floors Height of Condominium Buildings 10 Floors 100 Floors Height of Affordable Housing Buildings 10 Floors 100 Floors Height of Offices 10 Floors 100 Floors Height of Hotels 10 Floors 100 Floors

3.4 ECONOMIC OBJECTIVE

The first of the three objectives considered is the Economic Objective. Put simply, this is a maximum-seeking function which divides Profit by Construction Cost, endeavoring to calculate simple return on investment. In reality, the construction timeframe for taller buildings would necessitate having financing for a greater amount of time. However, a measure which includes time of construction, such as Internal Rate of Return, would add a confounding variable. Similarly, to a developer, all return would not be created equally, as the project may be funded by debt, equity, and levels of mezzanine financing. This simple maximum-seeking algorithm does not seek to divide the capital stack into its constituent parts and makes no distinction between return on debt and return on equity.

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Necessarily, the economic model used is not reflective of actual tall-building pro-forma, which are typically multi-period models which evaluate cash flows over the process of land acquisition, design, construction, and stabilization. A single-period simplified model has been used for two primary reasons. First, many of the variables in the more complicated multi-period pro-forma would be constant across multiple solutions. For example, any land acquisition cost added to the “front end” of construction would, in the case of the project selected, not change between design scenarios. This could have the effect of dampening the model’s responsiveness to variables. Second, a more complex model may require evaluation input which is not readily available and is outside the scope of this investigation. For example, ascertaining how quickly rent rolls in residential buildings become stabilized may represent an entire research project unto itself and is not sufficiently germane to the effects under investigation to be exhaustively elucidated.

3.4.1 Building Costs

In considering the parts of this calculation, let us first consider building cost. For our purposes, base costs were calculated on a per-unit-floor area basis using Marshall Swift Cost

Estimation Calculator Method (CoreLogic 2019). These costs are based on “final costs to the owner and will include average architects’ and engineers’ fees.” (Ibid.) They include all material and labor costs, sales taxes, interest on building funds during the period of construction, normal site preparation including finish, grading, and excavation for foundation and backfill for structure only, utilities from structure to lot line, and contractor’s overhead and profit. This method explicitly excludes the cost of assembling lands, land planning costs, special foundations, discounts and/or bonuses, off-site costs, furnishings and fixtures, marketing costs, and general contingency (Ibid. 1.3).

Land costs are explicitly excluded from the model, as in the case considered they are likely consistent across scenarios. Of course, the whole impetus for tall buildings involves the

37 scarcity of land and its intrinsic values in dense urban settings. Further research would be necessary to incorporate the cost of land were the project to have variable site or footprint characteristics.

For Residential and Hospitality Buildings, Class B Structure (Cast-in-Place Concrete) is selected, as it is more economical on a per-unit-square foot basis. For Office Buildings, Class

A Construction (Steel Composite) is utilized. While it is less economical by 1.8%, it is more commonly observed and suitable for office spans, as discussed infra. For market-rate housing,

Luxury High-Rise value for Good Quality is utilized. For Office, Good Quality is utilized, intending to imitate Class-A office space. For Affordable Housing, Apartment High-Rise is substituted for Luxury High-Rise, although Good Quality is still utilized. As dictated by the calculator method, all values include an approximation of the cost of fire-sprinklers on a per- unit-floor area basis. As this latter cost is dynamic with building size, a middle-height building is utilized to avoid separately calculating this value for each potential building height.

However, due to the height of the buildings, calculating merely a square footage cost is insufficient, as costs are dynamic and affected by building height. Costs tend to rise as the building height rises due to the increase of structure necessary to ensure rigidity, the cost of vertical communications, the cost of fire systems, and the cost of plumbing and electrical systems (Zelazowski 2015). Inversely, groundwork, foundations, and roofing costs tend to decease with height, as each assembly is being divided over a greater floor area (Ibid.). De

Jong and van Oss (2007) calculate the premium for each ten floors to be 8% on a unit-floor-are basis. While this study focused on Holland and not Chicago, the model it offers is sufficiently simple for the calculation method selected. Also, applying the 1.008 multiplier (an 8% premium divided by ten total floors), a cubic relationship is achieved, rather than the simple linear relationship recommended by Marshal and Swift. Figure 6 below shows the relationship

38 between height and cost created by this multiplier effect. Figure 7 shows how this impacts the costs of each Building Type for a ten, forty, seventy, and one hundred story building.

Cost Premium for Height 240%

220%

200%

180%

160%

140% Per SF Cost Multiplier Per SF Cost 120%

100% 1 112131415161718191 Height in Floors

Figure 6: Cost Premium per Height using Multiplier Method

Cost/SF by Building Type and Height

Apartment Condominium Affordable Housing Office Hotel

$700.00

$600.00

$500.00

$400.00

$300.00 Cost per SF $200.00

$100.00

$- 10 40 70 100 Height of Building in Floors

Figure 7: Per-Unit-Floor Area Costs by Building Type and Height

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Measured against total building cost is net present value. Net present value is calculated as gross operating income divided by a capitalization rate. The capitalization rate represents the ratio between a buildings income and value. Capitalization rate is taken from the Cushman & Wakefield Mid-2017 Capitalization Rates by Property Type survey for

Multifamily and Office Buildings (Cushman & Wakefield, 2017). The capitalization rate for

Central Business District Class-A Office space in Chicago is approximately 6.67%. For

Multifamily Residential (which will be applied to Apartment, Affordable, and Condominium) a value for Chicago is not provided, so 5.2% is utilized, approximating the mid-points of the ranges provided for surrounding cities. For Hotels, the CBRE North American Capitalization

Rate Survey is utilized, showing a Central Business District, Full-Service Hotel Capitalization rate of 8.28% (Baker 2019).

Capitalization Rate is applied to Net Operating Income (NOI). For our purposes, NOI

Consists of the Gross Floor Area multiplied by the efficiency off the floor plate, multiplied by the occupancy percentage, multiplied by the per-net-square-foot annual revenue. For condominiums, which are for-sale rather than rental, a simpler model accounting for sale price rather than annual rent is utilized. Rents and sale prices are also cost-adjusted.

3.4.2 Building Revenues

In calculating the height premium for residential market-rate rentals, three buildings are considered. In each building, the rental rate, unit size in square feet, and floor level are gathered from the buildings website. As each of these buildings is complete, it is possible that data gaps exist in the forms of units which have already been rented. It is also possible that the units which have not been rented actually represent less desirable price points than those which have been rented. However, since precise rental rates are generally not publicly published, this investigation will assume that the advertised rates are an accurate representation of market conditions.

40

The three buildings in question are NEMA Chicago, located at 1210 South Indiana

Avenue in Chicago, Essex on the Park, located at 808 South Michigan in Chicago, and The

Cooper Southbank, located at 720 South Wells in Chicago. While all projects are nearer to the

Central Business District than The 78 site, they represent the nearest new-construction buildings with sufficient height for investigation for which unit prices, floors, and areas are available. Figure 8 shows the per-unit-floor area rental price by floor for each building. Table 2 summarizes the number of points, y-intercept, and slope of each graph. To ensure each project is weighted equally despite having an unequal number of data points, linear regression is performed for each model, with the appropriate values averaged to create a “predictive” relationship for height vs. residential rent. General Building information is provided by The

Skyscraper Center.

41

NEMA Available Rents

$7.00

$6.00

$5.00

$4.00

$3.00 y = 0.0182x + 3.5215 R² = 0.4652 $2.00

Montly SF per Net Montly Rent $1.00

$- 0 1020304050607080 Floor of Unit

Cooper Available Rents

$4.50 $4.00 $3.50 $3.00 $2.50 y = 0.0108x + 3.3113 $2.00 R² = 0.1248 $1.50 $1.00 MonthlyRent SF per Net MonthlyRent $0.50 $- 0 5 10 15 20 25 30 35 Floor of Unit

Essex Available Rents

$6.00

$5.00

$4.00

$3.00 y = 0.0175x + 3.055 $2.00 R² = 0.3345

Monthly SF per Net Rent Monthly $1.00

$- 0 102030405060 Floor of Unit

Figure 8: Relationship between Unit Height and Rental Price per unit Floor Area for Three Buildings

42

Table 2: Summary of Buildings and Regression Date for Residential Rental Model

Project Name Total Floors Number of Units Available Line Slope Y-Intercept

NEMA 81 Floors 121 of 800 Units 0.0182 3.52 Chicago Cooper 29 Floors 53 of 473 Units 0.0108 3.31 Southbank Essex at the 57 Floors 94 of 479 Units 0.0175 3.34 Park

A similar technique can be utilized for residential for-sale units (Condominiums). In this case, it is the per-unit-square foot advertised sales price which can be compared to the floor height and area of any given unit. As condominiums often involve pre-sales, two under- construction buildings were added to one existing building. The former use initial sale prices while the latter uses units available for re-sale. While re-sale may be less accurate, due to the limited number of condominium projects of suitable height which have been constructed in recent years, it is necessary to utilize this data also. The projects selected are 1000 Michigan located at 1000 South Michigan in Chicago, Legacy at Millenium Park, located at 60 East

Monroe Street in Chicago, and Vista Tower Chicago, located at 345 East Wacker in Chicago.

Figure 9 shows the regression analysis by building. Table 3 summarizes the collected data used for the predictive model.

43

1000M Price by Floor $1,600.00 $1,400.00 $1,200.00 $1,000.00 y = 12.89x + 447.98 $800.00 R² = 0.7762 $600.00 $400.00 Sale Price per SF $200.00 $- 0 20406080 Floor of Unit

Legacy Price by Floor $900.00 $800.00 $700.00 $600.00 $500.00 $400.00 y = 6.0716x + 377.09 $300.00 R² = 0.4101

Sale Price per SF $200.00 $100.00 $- 0 20406080 Floor of Unit

Vista Price byFloor $2,000.00 $1,800.00 $1,600.00 $1,400.00 $1,200.00 $1,000.00 y = 14.594x + 509.24 $800.00 R² = 0.8607 $600.00

Sale Price per SF $400.00 $200.00 $- 0 20406080100 Floor of Unit

Figure 9: Relationship between Unit Height and Sale Price per unit floor area for Three Buildings

44

Table 3: Summary of Building and Regression Data for Residential For-Sale Model

Project Name Total Floors Number of Units Counted Line Slope Y-Intercept

1000 Michigan 74 Floors 31 Units 12.89 447.98

Legacy at 72 Floors 16 Units 6.07 337.09 Millennium Park Vista Tower 101 Floors 22 Units 14.36 509.24 Chicago

For office rental rates, a suitable number of advertised suites of comparable sizes in

Class-A Buildings was not publicly available for review. While some prices were listed publicly for some buildings, the sizes of the units, ages of the buildings, and other confounding variables made calculation with the method used for residential rents and sale prices impractical for office rents. Instead, for base prices, a media report from 2019 indicates that office rental rates are now “over $40 per square foot” (Roeder 2019). This is consistent with office listing site squarefoot.com’s assessment of Chicago Rentals, which indicates that Class-

A space in the West Loop, Central Loop, East Loop, River North, Fulton Market, and North

Michigan Avenue sectors are $51, $45, $43, $50, $53, and $44, respectively. For simplicity, we will use a base price of $50.00 per net square foot.

As for premium for height, Barton and Watts (2013) note that while value is added per floor for residential projects, the relationship in offices is less clear, opining that “There can be little doubt that a tall office building attracts a premium of some nature, but articulating what that is, and how it relates to particular levels, would seem to be less than straightforward.”

Koster et al (2011) similarly note that “To what extent taller building receive rent premiums, and more specifically, whether there is a reputation premium, is unknown.” Their study, conducted in Amsterdam, Brussels, and Utrect, indicates that “firms are willing to pay about 4% for a 10%

45 increase in building height.” Further, they find that a “landmark effect” for buildings at least six times the average height is “about 17.5%” (Ibid.).

Clearly, some premium for height is warranted, however the precise magnitude remains elusive. For the purpose of this investigation, it will be assumed that as height of the building increases (measured by the height of the average floor, or the total height divided by two), the total rent for all floors of the building will increase 0.725%. This accounts for some modest rent premium while conceding that the premium is likely substantially less than that observed in residential construction.

As with Offices, the relationship between the value of Hotel Rooms and their respective height is unclear. Anecdotal evidence suggests that higher-floor rooms are more valuable and command some sort of premium, but this effect could be due to the tendency to locate suites or deluxe rooms on upper floors. In any case, while the existence of some premium for height seems likely, there appears to be no support for such a notion in the literature. Therefore, for the purpose of this exercise, a hotel room located on the tenth floor and one on the 80th floor will be considered to command equal rent.

For affordable housing, rent does not appear to be a factor of market forces. Rather, the Chicago Affordable Housing ordinance considers housing to be affordable if a household earning 60% of the area’s median income can pay rent with 30% of the household’s gross income (City of Chicago, 2014). In Chicago, 60% of Area Median Income is $41,041.80 for a family of four. 30% of this amount is $12,312.54 per year, or $1,026.05 per month. In market- rate rentals, the average unit size for units observed as part of the height investigation conducted above indicated an average rental unit size of 972 square feet. This suggests a per- unit-square foot affordable rent of approximately $1.05 per square foot per month.

Revenue for Hotel Rooms is calculated using published values for Chicago’s Average

Daily Rate and Occupancy for 2018. These values are $210.64 and 75.2%, respectively (Miller

46

2018). Multiplying these values provides RevPar, a per-key, per-night revenue expectation for each hotel room. The number of rooms, which is to be multiplied by RevPar and 365 days per year, is calculated by dividing the net square footage of the proposed hotel by the average hotel room size times a multiplier to account for non-room spaces. While the average hotel room size in the United States is approximately 330 square feet (Castillo 2015), this figure does not include other spaces, such as meeting, lobby, amenity, and back-of-house spaces, which still represent net square footage. Therefore, the net floor area in square feet of each hospitality building is divided by 450 to achieve a reasonable estimate of the total number of rooms. This figure does not include net-to-gross losses, which are discussed in greater detail below.

3.4.3 Building Efficiency Loss and Vacancy Loss

The model must also account for the loss of efficiency implicit to tall buildings. As building height climbs, a greater amount of space is needed for structure and vertical circulation. Barton and Watts (2013) quote Langdon in providing values for buildings ranging from 0-50 stories and efficiencies ranging from 81-84% for “short” buildings to 75-79% for taller buildings. Sev and Ozgen (2009) add another ten data points by measuring the floor plate efficiencies of ten tall buildings around the world with floor counts varying from 69-110 and efficiencies ranging from 60% to 77%. Considered together, these studies provide fifteen data points upon which linear regression can be performed. The implied efficiency relationship is shown in Figure 10 below. It is worth noting that both surveys consider Office Buildings, which have different structural and vertical circulation requirements from the other building types considered. However, due to lack of efficiency and height data for other building types, the office relationship will be utilized, creating a possible source of error. It is also worth noting that the values in question are on a per-floor-plate basis and do not consider mechanical spaces,

47 lobby spaces, etc. For the purpose of this investigation, these spaces are assumed to impact all buildings equally.

Height vs. Floor Efficiency 0.9

0.85

0.8

0.75

0.7

0.65

0.6 y = -0.0014x + 0.8145 0.55 R² = 0.4913

0.5 0 20406080100120

Figure 10: Floor Count vs. Floor Plate Efficiency with data from Barton and Watts (2013) and Sev and Ozgen (2009)

Of course, even converting gross floor area to net floor area using an efficiency factor does not result in a number of square feet which will ultimately be rentable or salable, as some space will be vacant as a structural cost of doing business. Vacancy for hotel rents has been discussed above. Vacancy for for-sale units is considered to be a risk to the owner and not the developer, and thus is not considered. Vacancy rates for Office and Rental Properties, however, must be accounted for. For the purpose of office vacancy, resources from early 2019 indicate that cumulatively Class-A office vacancy in Chicago’s Downtown Market are approximately 13% (Ecker 2019). Apartment vacancy can be estimated through the Census

American Community Survey program, which most recently reports an apartment vacancy rate for Chicago of 6.18%. Ergo, for the purpose of this exercise, offices will be said to be 87% occupied while apartments will be said to be 94% occupied.

3.4.4 Operating Expenses

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In order to devise a Net Operating Income, one must subtract expenses from revenue.

Of course, building expenses are complicated, multi-faceted, and vary by building type, size, and management. Ergo, this study will apply a simple per-unit-floor area or per-unit expense model, depending on type, to devise operating expenses. For for-sale residential

(condominium) buildings, ongoing expenses are considered to be the purview of the Home

Owner’s Association and therefore are not considered. Hotel operating expenses are calculated on a per-key basis using the 2017 value for U.S. Limited Service Hotels annual expenses of $21,390 per operating room (Mellen 2019). Rental and Office expenses are calculated on per-unit-floor-area basis. For Apartments, the National Apartment Association

Survey data suggests annual operating expenses of $9.12 per square foot, excluding Capital

Expenditures (Munger and Smith, 2019). The Building Owners and Managers Association reports per-square-foot operating annual expenditures for Office Buildings at $12.47 per square foot, including Cleaning, Utilities, Fixed Expenses, Parking Expenses, Roads and Grounds

Maintenance, Building Repair and Maintenance, and Real Estate Taxes (BOMA 2018).

As a matter of necessity, operating expenses are simplified to reflect information that is readily available and directly affected by building square footage. It is not intended to be an exhaustive catalog of the way in which buildings effect the profitability of organizations. It may be, for example, that the greatest “operating expense” for any building is in fact the staff that work within the building. It is possible that changing architectural variables could change staff retention or other dependent variables not considered that have a greater impact on organizational profitability than the simple maintenance costs observed here. The relationship between these broader economic variables is important to understand, but it outside the scope of the current investigation.

3.4.5 Economic Cost Function

Considering all above factors, the cost function for each building type is as follows:

49

Maximize RR = ((NOI / Cap) – IC) / IC

Where: RR = Rate of Return

NOI = Net Operating Income

Cap = Capitalization Rate

IC = Initial Cost

For rental projects, Net Operating income is further defined as follows:

NOI = SF x Eff x Rent x Occ (SF x Opp)

Where: SF = Gross Floor Area

Eff = Floor Plate Efficiency

Rent = Revenue per net square foot of floor area

Occ = Occupancy Percentage

Opp = Operating Expenses per gross square foot of floor area

For For-Sale Residential (Condominium), no capitalization rate or net present value calculation is necessary. Instead, the sale price is simply divided by the initial cost to find the rate of return.

3.5 ENVIRONMENTAL OBJECTIVE

Stated plainly, the goal of the environmental objective is to measure life-cycle greenhouse gas emissions associated with operational energy, embodied energy, and transportation energy for any given solution. While it is certain that environmental objectives outside of the reduction of greenhouse gas emissions exist (e.g. water conservation, air quality), for the purpose of this investigation, the “common thread” in measuring each type of environmental impacts will be tons of carbon dioxide equivalent gases emitted over a fifty year time-frame.

50

The environmental objective is to minimize the total emission. It does not consider what alternatives these buildings may be replacing. For example, it is possible that by adding occupants in a dense urban setting removes those occupants from more carbon-intensive means of living, thereby providing a net environmental benefit. However, comparing the relative carbon intensities at different densities in this manner is beyond the scope of this investigation, which seeks only to minimize net lifecycle carbon emissions. For the purpose of this exercise, adding one more square foot or one more occupant will always raise carbon output. This raises the question of whether or not the model will simply try to minimize the total gross floor area, rather than selecting for the most appropriate environmental option. This exigency is dealt with using the penalty functions explained in Section 3.7 below.

While a lifecycle model is used for the environmental objective, the economic and social objectives rely on a simpler “snapshot” in time and do not consider the entire lifetime of the buildings. This is the case primarily because current and future emission can be discussed in the same terms (i.e. tons of greenhouse gasses) while talking about economic or social performance longitudinally is more difficult. For example, considering the lifecycle economic impact would require knowledge of the discount rate to account for the time-value of money, the escalation rate for construction expense, and changes in consumer demand over time.

Such lifecycle analysis may be fertile ground for future research, but here it is not considered.

3.5.1 Operational Energy

Operational energy is approximated using the New Construction Commercial Reference

Buildings offered by the Department of Energy’s Energy Efficiency and Renewable Energy

Office (2012). These reference buildings offer Energy Plus input files for ASHRAE 90.1 compliant buildings of sixteen types. For the purpose of this exercise, the most recently updated reference buildings, dated November 13, 2012, were utilized.

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Building simulations are provided for each ASHRAE Climate Zone. For this problem,

Climate Zone 5A, the zone corresponding to Chicago, was selected. The models chosen for residential projects was the “Midrise Apartment” model, with a gross floor area of 3,135 square meters. Office was simulated with the “Large Office” model, which measured 46,320 square meters. Hospitality used the “Large Hotel” Model, encompassing 11,345 square meters of gross floor area. In all cases (except perhaps the Large Office), the square footages of the reference buildings are likely smaller than the solutions proposed. This can be partially accounted for by using a per-unit-floor-area technique for calculating total operational energy.

However, as smaller buildings may expend less energy on certain elements (e.g. vertical circulation), use of the reference buildings remains a possible source of some modest error.

Simulation results are provided in terms of kilowatt hours of electricity and therms of natural gas for each model. While this is provided in Site Energy, a site-to-source factor is also provided, which is 3.546 for Electricity and 1.092 for Natural Gas (Ibid.). Carbon equivalencies for both Electrical Generation and Natural Gas are calculated using the Environmental

Protection Agencies Greenhouse Gases equivalency calculator (EPA 2019). A total emissions per gross square feet of floor area by energy source is shown in Figure 11. Multiplying the gross floor area by the above per-unit-floor-area emissions for a span of fifty years yields the total carbon dioxide equivalent emissions for the lifetime of the building.

52

Per SF GHG Emissions by Building Type 0.07

0.06

0.05

0.04

0.03

0.02

0.01

CO2E Emissios per Emissios CO2E Year per SF Floor Area 0 Apartment Condo Affordable Office Hotel

Electricity Gas

Figure 11: Greenhouse Gas Emissions per Unit Floor Area for Subject Building Types

3.5.2 Embodied Energy

In addition to the operational energy used by each building, there exists some embodied energy associated with the construction of the building and the support infrastructure surrounding the building. For the purpose of this investigation, embodied energy will be limited to the embodied energy of the building’s themselves and will not include surrounding infrastructure. While a greater density of buildings will likely require more infrastructure, some base level of road and utility access will be required regardless of the height of the building. Determining the marginal increase in infrastructural embodied energy associated with each modality is outside the scope of this research. Likewise, some material is likely to be replaced as part of periodic capital expenditures or ongoing operations and maintenance within the building within the 50-year time horizon considered. Once again,

53 determining the percentage of replacement and the embodied energy of these materials is outside the scope of this investigation.

To simplify the embodied energy calculation, an adjusted per-unit-floor-area metric is used. Thiel et al (2013) compare the embodied energy of a net-zero building to five existing case studies. For the purpose of this study, the five case study buildings are of greater interest than the comparison itself. The uses of the five buildings considered range from Office to

Laboratory and Residential Space. Their heights vary from three to 38 floors, with only a single building being greater than six floors in height. Their structural systems and envelope materials are also varied, providing a useful average. Evaluation of these precedents shows a distribution centered around 350 kg of carbon dioxide equivalent emissions for each square meter (Ibid.). Converted to imperial units, this suggests as “baseline” figure of 0.325 tons of

CO2E per gross square foot.

As explained above, taller buildings require a disproportionate amount of materials to account for exponentially growing lateral loads and the requirements of vertical circulation.

This “premium for height” phenomenon must also be accounted for in embodied energy.

While heating and cooling of one square foot of space on the tenth and ninetieth floor of a given building require roughly the same amount of operational energy, the energy required to build these square feet varies substantially. Foraboschi et al (2013) calculate the embodied energy of buildings of 20-70 floors in height using different structural systems to estimate the premium for height for embodied energy. Their paper also includes an investigation of the impact of some lightweight floor systems. For the purpose of this paper, the most interesting results are those for steel-concrete and reinforced concrete buildings. One of the three lightweight systems considered is also included in the calculations below. A summary of findings is presented in Figure 12.

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Structural Embodied Energy Premium for Height 160%

140%

120% y = 0.0098x + 0.7616 100% R² = 0.933

80%

60%

40%

20% % Increase in EE per unit floor area per floor unit in EE % Increase 0% 0 1020304050607080 Number of floors

Figure 12: Embodied Energy Premium For Height, Calculated based on Values from Forabachi et al (2013)

Performing linear regression on five height cases in this study provides a road-map on how to scale embodied energy per unit height. It is worth noting that the referenced work includes only the columns, core, primary and secondary beams, and floors. It is likely that including only these structural elements represents a “worst case scenario” for premium for height, as one might expect finish and envelope materials to have a lesser premium or no premium at all. Unfortunately, information on premium for height for non-structural materials is not well defined in the literature.

3.5.3 Transportation Energy

Of the three types of energy considered herein, transportation is the most difficult to quantify and requires a myriad of assumptions about transportation use. A meaningful effort to quantify transportation may begin with an effort to number quantity of people using each type of building and assigning a an amount of transportation energy to each occupant.

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For residential projects, occupancy is based on the investigation of unit sizes and locations conducted in Section 3.4 above. This investigation showed an average rental unit size of 972 net square feet. Condominium (for sale) units were found to be much larger. These units measured, on average, 2173 net square feet per unit. No distinction was made between affordable and market rate rental unit sizes. To calculate occupancy, the net floor area of each proposed building was multiplied by the occupancy of the building and then divided by the unit sizes above. Each unit was said to have 2.6 occupants, the national average U.S. Household size (Fry 2019). It is possible that multi-family households have a different number of people or that regional variation exists in this number, but accounting for this variance is outside the scope of the present investigation. It is also possible (perhaps even likely) that some share of such urban multi-family units are only partially occupied, being second homes or peid-á-terres for the wealthy. For the purpose of this exercise, all units are assumed to be occupied full time.

For offices, the Commercial Real Estate Development Association tracks the number of net rentable square feet per office employee, finding most recently that the average office has

181 net square feet per employee (Ponsen 2019). For hotels, the number of rooms is estimated by the same method as in the revenue model, by dividing the total gross square footage by 450 square feet. This is then multiplied by 1.6 persons for room. This accounts for both a guest in the occupied room and staff, which is estimated at 0.5 to 1.0 persons per room

(Vallen and Vallen 2018). The cumulative net floor area for each occupant (not accounting for vacancy loss) is shown in Figure 13.

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Net SF per Occupant by Use Type 0 100 200 300 400 500 600 700 800 900

Apartment 305

Condo 843

Affordable 305

Office 181

Hotel 281

Figure 13: Net Floor Area per Occupant, not accounting for vacancy loss

Given the ability to calculate the number of occupants, the next step is calculating how much of each occupant’s transportation energy should be dedicated to the proposed development. For office, 40 hours/week is a safe assumption. Given the total of 168 hours in each week, office users transportation emissions are to be assigned at a rate of 24%. Similarly,

Residential use fraction is estimated at 121 hours per week (72%) and hospitality use is estimated at 84 hours per week (50%). Multiplying per capita fuel use by these usage fraction ensures that only a portion of the fuel consumed by the average user is attributed to the development. Stated another way, if a given person works in the development, but does not live there, only 24% of his/her fuel usage will be accounted for, corresponding to the fraction of his/her time estimated to be spent at the development. Figure 14 shows time usage fractions for each of the subject building types.

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Percentage of Transportation Use for Each Type 80%

70%

60%

50%

40%

30%

20%

10%

0% Apartment Condo Affordable Office Hotel

Figure 14: Use Fraction multiplies applied to fuel consumption of each building type

According to the US Department of Transportation (2019), per capita fuel consumption in Chicago, IL measures 206 gallons of gasoline per person per year and 37.42 gallons of diesel fuel per person per year. The Environmental Protection Agency (2019) greenhouse gas calculator reports that each gallon of gasoline burned releases 0.008887 metric tons of carbon dioxide while each gallon of diesel fuel burned releases 0.010180 metric tons of carbon dioxide.

3.5.6 Environmental Cost Function

The total environmental impact of the project can be given by the following equation:

Minimize GHGTOTAL = GHGOPP + GHGEE + GHGTRANS

Where:

GHGTOTAL is total Greenhouse Gas Emissions

GHGOPP is total Operational Energy

58

GHGEE is total embodied energy

GHGTRANS is total Transportation Energy

Each of the three terms represents the summation of the total of all buildings in a given solution.

3.6 SOCIAL OBJECTIVE

While energy and economics can be evaluated in bottom line terms (in greenhouse gas emissions and rate-of-return, respectively), it is much more difficult to discuss the abstract notion of social good in terms of a single, bottom-line value. Social good tends to be amorphous and difficult to assign a singular, objective value.

Social benefit may also be realized through design at a scale which is not feasible to measure in the current model. It could be, for example, that having common spaces more readily visible would enhance social interaction, but the variables considered in the model are agnostic to this quality. Even where the scale is appropriate, the direct social benefits are not always directly measurable. For example, it may be that having taller buildings which result in leaving more of the site “open” for recreation or public use would be the most equitable outcome. However, the relationship between open space preservation and social equity is difficult to quantify. It is unclear, for example, what portion of land should be reserved for public use and if this percentage would relate linearly to beneficial social outcomes.

Complicating matters further is that the known determinants of social sustainability do not necessarily impact the variables in the neighborhood-scale model. Kamail and Hewage

(2015) identify twelve social “Sustainability Performance Indicators,” yet several of these indicators are not of suitable scale for considering here. For example, the indicator “cultural and heritage conservation” would not appear to apply to the vacant site considered in this investigation, at least not in as much as it would affect height and use choices. “Safety and

59 security” are likewise difficult to relate to the height and use variables, as it appears possible to have safety and security in any arrangement selected.

Maleki et al (2019) evaluate the social sustainability specifically for high-rise buildings.

In addition to safety and security, they identify sense of belonging to place, comfort, and aesthetics as criteria of social sustainability. Each criteria is then assigned further determinants. Determinants for comfort, for example, include thermal comfort, user flexibility, healthcare, noise pollution, natural lighting, and indoor air quality. While it may be easier to achieve thermal comfort or natural lighting with a given density of buildings, these variables would seem to be more suited to building-scale investigation. Healthcare availability and noise pollution seem unlikely to be factors which would be impacted by the number, height, and type of building selected. Perhaps a more complete analysis could identify connections between building massing and, for example, noise pollution, but such investigation is outside the scope of the current problem.

Stender and Walter (2019) develop a social sustainability matrix by which to evaluate existing projects on three categories: accessibility, social cohesion, and participatory process.

One again, ideas like inclusion and participation may be integral to social sustainability, but their connection to the selection of building types and heights is, at best, poorly understood and at worst, totally intractable. The Framework for Strategic Sustainable Development seeks to measure social well being and at least claims to have some measure of external validity

(Broman and Robert 2015). In addition to some ecological measures, this framework evaluates

“structural obstacles to health, influence, competence, impartiality, and meaning-making.”

Removing obstacles to learning, free speech, safe working conditions, and the creation of common meaning would seem to be laudable goals. However, their aim appears to be more a question of social structure than of building massing.

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Murphy (2012) review eight bodies of literature contributing to “the social pillar of sustainable development.” These include United Nations and European Union Policy

Documents, Multi-lateral Sustainable Development Indicators, Social Sustainability Literature,

Green Social Policy Literature, and Environmental Justice Literature. Murphy proposes four

“organizing dimensions” common to much of the literature: equity, awareness for sustainability, participation, and social cohesion. These dimensions seem likely to be impacted to some degree by density and building use, but Murphy’s investigation appears focused on policy implications rather than on elucidating design objectives for the built environment.

More interest in urban forms, perhaps, is shown by Eizenberg and Jabareen (2016) who identify Urban Forms as one of four major aspects of social sustainability, along with Equity,

Safety, and Eco-Prosumption. The paper recommends urban forms which promote “a sense of community, place attachment, a sense of safety, and healthy communities.” While these objectives may seem nebulous, several concrete steps are provided, including compactness, density, mix of uses, and passive solar properties. However, little is done to validate the connection of these steps to social outcomes. While it is argued that “these typologies compose the structure of desired sustainable urban form,” too little empirical evaluation of these metrics is provided to establish a relationship between these concrete recommendations and optimizing neighborhood-scale problems.

Some determinants of social sustainability do consider the height and use variables in subject model, but are otherwise unsuitable for measuring social sustainability meaningfully.

Du et al (2017) compare life satisfaction (“one of several key measures of social sustainability”) between low and high-density urban settings, finding that high-rise residents reported higher life satisfaction when controlling for demographic variables. However, the type of housing explained less than 5% of the observed variance. Furthermore, even controlling for demographic variables, such a small margin could easily be explained by another confounding

61 variable, such as access to urban amenities or more robust urban employment prospects. An argument that greater height leads to greater satisfaction does not appear to be supported by the literature.

Al-Kodmany (2018) also appears interested explicitly in the social sustainability of tall building developments, even relying on the economic, social, and environmental framework for defining sustainability. This framework, to its ultimate credit, identifies several means of improving social sustainability in tall buildings including improving resistance to terror attacks, minimizing disparity in the quality of life of residences, and even cleaning windows to allow for visual connection to the public realm. It is certainly possible that height and mixes of uses affect resiliency and public connectedness. However, it is also possible to argue that buildings of any of the proposed heights and types can offer seismic and fire safety. Similarly, buildings in any configuration considered in the subject optimization algorithm can have clean windows.

Perhaps the most valid means of evaluating the social sustainability ramifications of the chosen design objectives is not offered by any singular framework, but rather draws on the recurrent themes in a number of frameworks. The following section will argue that Housing

Affordability, Employment Opportunity, and Tax Base generation are three measurable ways of assigning value to a given solution within the proposed framework. These three determinants are not selected arbitrarily, but rather represent recurring themes in the literature of social sustainability evaluation.

3.6.1 Housing Affordability

Of the social criteria considered, perhaps the most directly supported in the literature is the call for some component of affordable housing, by this or another name. Kamali and

Hewage (2015) cite affordability among their Social Sustainability Performance Indicators

(although not explicitly affordable housing). Stender and Walter (2018) explicitly include affordable housing in the accessibility portion of their Social Sustainability Framework. The

62 provision of shelter is cataloged among signs of the development of equity in Murphy’s (2012) analysis, which also notes that housing provision is among the Social “Themes” of the United

Nations Commission for Sustainable Development. Eizenbern and Jabareen (2017) suggest inclusion of a greater diversity of housing types and incomes promotes Social Sustainability.

Al-Kodmany (2018) cautions against the creation of tall buildings solely as “mansions in the sky,” which are said to “reinforce social and racial segregations.”

The question then is not whether or not affordable housing is important to Social

Sustainability, but rather how it can be measured convincingly in the present model.

Enhancements to the Affordable Requirements Ordinance (City of Chicago, 2014) provide a useful frame for defining affordable housing for rental units as “affordable for households earning up to 60% of the Area Median Income… for a term of 30 years.” Given the high-rise mix is likely to consist of one through three-bedroom apartments, HUD’s Maximum Affordable

Rent for 2019 for the City of Chicago suggests monthly rent of $928 for a one-bedroom, $1,110 for a two-bedroom, and $1,281 for a three-bedroom (where tenant pays utilities) (HUD 2019). If one assumes a precise unit mix is unknown, then taking 30% (the fraction of net income allowable for housing cost) of 60% of Chicago’s Area Median Income (as suggested by the

ARO), yields a monthly rent of $1,026.25. Dividing this by our average rental unit size of 972 net square feet, this suggests a per-square-foot, per-year rent of $12.67 (in line with HUD figures).

Considered conceptually, some percentage of the 13,000,000 square feet allotted for development can be spent on affordable housing units, with the bounds being 0% (no affordable housing component) and 100% (entire development consists of affordable housing).

Ergo, if one multiplies the gross square footage of the affordable units provided by the efficiency factor of height of the building purposed, and divides the results by the total number of possible affordable units (given by dividing the total development gross square footage by

63 the average unit size), one may arrive at a percentage of total development expended on affordable housing. Given the support for affordable housing in the literature relative to the other two metrics discussed below, provision of such housing is to be weighted doubly in calculating the social sustainability score.

3.6.2 Employment

More abstract than the direct provision of affordable housing is the notion that socially sustainable development should offer prospects for employment, thus contributing directly to the economic well-being of the host city. Kamali and Hewage (2015) suggest that contribution to the local economy is a key Social Sustainability Performance indicator. Broman and Robert

(2015) indicate that social sustainability requires that people “are not subject to structural obstacles to competence [including] obstacles for education and developing competence individually and together.” These goals, while abstract, can certainly be read to suggest the importance of employment.

Others are more direct in connecting employment to social sustainability. Murphy’s

(2012) literature review of the Pillar of Social Sustainability finds numerous suggestions of the importance of employment opportunities. Stender and Walter (2018) include employment along with education amongst their Accessibility categories in their social sustainability framework. Eizenbern and Jabareen (2017) identify a connection to employment, although they see it as a “non-physical” component of social sustainability.

Measuring prospects for employment could be accomplished by simply counting the number of full-time-equivalent jobs generated by each building. However, a simple count would be mathematically difficult to compare to the percentage values given for the other two measures of social sustainability (affordable housing above and tax base below). Therefore, as with affordable housing, a “percentage of possible total jobs” is suggested. It then becomes necessary to define how many “jobs per square foot” are offered by each building type.

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Staffing for multi-family buildings appears to vary widely, with the precise number of staff based on the total number of units in the building and the level or service provided to each unit. For rental properties, a very rough ratio of one staff person per fifty rental units is suggested by some contemporary accounts (Champagne 2012) to account for maintenance, leasing, and site management personnel. This ratio seems suitable for market-rate-rental and affordable properties, where the number of staff can be calculated by dividing the net square footage of the building by the average unit size by 50 units per staff person to find a total number of staff. For-sale multi-family will still requirement management and maintenance, but will likely not require ongoing leasing personnel. Therefore, for the sake of simplicity, the model will use a smaller ratio of 75 units per staff. Along with the much higher average unit size, this makes for-sale residential buildings much less employment intensive on a per-unit- floor-area basis.

As stated above in calculating Office Census, Ponsen (2017) suggests a ratio of 181 net square foot of office space to one office employee. Ergo, the number of office employees per net square footage of office space can be found by simple division. This turns out to be the most employment-intensive type of use considered here. Using the value of 450 net square feet per hotel key calculated above, a number of hotel rooms per building area can be calculated. Vallen and Vallen (2018) suggest a room-to-staff ratio of 0.625 for select service hotels. This means the number of employees for hotels can be calculated by dividing the net floor area by the floor area per room and multiplying by 0.625. Figure 15 shows the net floor area per employee for each building type.

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Employees per 1,000,000 Net SF by Use Type

Employees (log scale) 1.00 10.00 100.00 1000.00 10000.00

Apartment 20.58

Condo 6.14

Affordable 20.58

Office 5524.86

Hotel 3555.56

Figure 15: Employees for a 1,000,000 NSF Building of Each Use Type

Using the above figures, a number of employees can be calculated for each building use case. As shown in Figure 15, Office use is the most intense case. Ergo, office use represents the maximum potential employment. To yield a percentage value, consider that the maximum possible employment for the development would be if all buildings were 100% office buildings. Such an arrangement would yield a total employment figure of approximately

71,823 employees. All other employment configurations can be measured as a percentage of this total possible. By measuring as a percentage, this value can be compared directly to the values for percentage of possible affordable housing and percentage of possible tax base.

3.6.3 Tax Base

Moving from the concrete to the abstract, it can be seen that affordable housing and employment represent key measures of socially sustainable development. Still more abstract, though, is the idea of generating suitable property tax base. While the benefits of jobs and

66 affordable homes can be seen immediately as social goods, the idea of developing more value for taxation is at least a second-order social good. That is to say, the immediate consequence of taxation is not particularly socially beneficial, but the schools, transportation network, and redistributive policies that taxes pay for can be of immense social benefit.

Of the Social Sustainability Performance Indicators identified by Kamali and Hewage

(2015), many appear immediately connected to benefits typically funded through taxation.

Specifically, the health of occupants, influence on location social development, cultural and heritage conservation, safety and security, and neighborhood accessibility and amenities are directly connected to taxes. Landmarks Ordinances, Police Patrols, Parks, and Public Transit access all are funded, to some degree, through local taxation. The security against crime, fire, and earthquake recommends by Maleki et al (2019) can be seen as analogues for well-funded police departments, fire departments, and code enforcement officials. Amenities, Connection, and Safety are keys to the Social Cohesion component of Stender and Walter’s (2018) Social

Sustainability Formwork. Once again, it seems a key provided of physical access, physical safety, and neighborhood-scale amenities is the local government. The Framework for

Strategic Sustainable Development (Broman and Robert 2015) promotes removal of obstacles to free speech, education, and partial treatment. Such justice and education outcomes are inexorably tied to locally-funded government. The participation and social cohesion axes identified by Murphy (2012) and the safety and equity axes identified by Eizenbern and

Jabareen (2016) appear similarly connected to the public sector.

If taxes are seen as a net social benefit, then one must ask what type of physical development will generate the most revenue. For the purpose of this investigation, property taxes are considered, as they provide the sort of local funding for the services enumerated above. Obviously, income taxes connected to employment and capital gains taxes connected to real estate development may also be impacted. As the connection between such taxes and

67 local services is less clear, they are considered outside the scope of this investigation.

Similarly, the calculation of property taxes is fairly opaque, will local millages varying by block and the assessed value of a given property being calculated via a complicated formula involving revenue, expenses, and exemptions. For the purpose of this model, the assessed value of a building on which taxes will be levied will be said to be identical to the net present value of the building for simplicity. Similarly, while individual owned housing units, multi-family properties, and office properties may all be taxed at different rates in different areas of the city, the reported average general tax rate of 6.786% will be utilized.

Once again, for the mathematical necessity of expressing the potential taxation as a value between zero and one (or as a percentage), the ratio of the tax liability of a given solution will be compared to the largest possible tax which could be collected, operating on the thinking that any solution will offer between 0 and 100 percent of the maximum possible tax benefit. By

“flexing” the model across a number of different scenarios, the maximum possible net present value per square foot was found to be $573.98. Therefore, the maximum possible tax collected is given by multiplying the prevailing tax rate by this net present value per square foot times the

13,000,000 total square feet.

3.6.4 Social Cost Function

The calculated social sustainability factor of the project can be given by the following equation:

Maximize SSF = ((2 x RAFF) + REMP + RTAX) / 4

Where:

SSF is the calculated Social Sustainability Factor

RAFF is the ratio of Affordable Housing in a given solution to the maximum

possible amount

REMP is the ratio of Employment Provided to the maximum possible amount

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RTAX is value of Tax Collected to the maximum possible amount

The algorithm will seek to maximize this value. That is, the algorhitm will favor values nearer to one and farther from zero, with zero and one being the minimum and maximum possible values respectively.

3.7 CONSTRAINTS

The variable outcomes of the model are constrained in two ways. First, the variable values themselves must meet certain criteria. Second, and more complexly, there exist penalty functions on each variable which seek to “move” the solution set towards 13,000,000 square feet as solutions are generated.

The simplest way in which the possible outcomes are constrained is via the “outside brackets” on the variable values themselves. Each building type can have only zero to ten buildings. Each building must be at least ten stories tall and a maximum of 100-storys tall.

These values are chosen to ensure that density suitable to the urban context is achieved and that possible solutions are not outside the realm of possibility. Both variables are treated as discrete. That is, it is impossible to have one half of one floor or one third of one building, so generated solutions feature only whole numbers.

Because the model seeks to provide outcomes which are closest to 13,000,000 square feet of gross floor area (the proposed square footage of The 78 Development), penalty functions are introduced to each variable. Put simply, solutions in the initial population are based on a random number generator. Thus, while they will fall inside the ranges provided, there is no way to ensure that they sum to the correct number of total square footage. The penalty function applied to each cost function serves to “nudge” each successive generation towards the desired number of square feet by apply a proportional “handicap” to its cost function scores.

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For the environmental cost function, for each square foot greater than 13,500,000 square feet or less than 12,500,000 square feet, a penalty of 10 tons of carbon is imposed.

This is intended to “direct” the evolving scenarios towards the appropriate square footage.

Therefore, if a proposed solution contained 14,000,000 square feet and generated 20,000,000 million tons of carbon dioxide equivalents, it would be adjusted by the difference between 14 and 13.5 million times ten, assigning this solution a value of 35,000,000 million tons of carbon dioxide equivalents. A similar principle is used for the Sustainability Score cost function, albeit of a much lower magnitude. In these cases, each square foot subtracts 1.0 x 10-7 from the cost function outcome. The same value is applied to the return on investment cost function in the economic model.

These values are calibrated specifically for the problem at hand. Using penalty functions which are too large causes the model to converge on solutions with the correct floor area without sufficient regard to their performance in the cost function categories. Using penalty functions too small does not penalize solutions with too small or too large of floor areas enough, allowing them to potentially become first-rank solutions.

3.8 MODEL SCENARIOS

In the following chapter, data will be provided for four scenarios. The parameters for each of these scenarios is expounded upon in the first portion of Chapter four below. Three single-objective scenarios will evaluate a breadth of solutions based on each of the cost functions enumerated above (one each for Social, Environmental, and Economic sustainability). Settings for the single-objective runs are shown in Table 4 below.

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Table 4: Single-Objective Genetic Algorithm Settings

Setting Value Algorithm Standard Genetic Algorithm Seeds and Repetition Random Seed (Mersenne Twister) Termination Criteria Perform 50,000 Function Evaluations Initial Population 500 Selection Method Tournament Selection with Tournament Size 10 Crossover Method Double Crossover with a Probability of 0.7 Mutation Method Jump (Probability 0.1) and Creep (Probability 0.75) with Elitism of 1 The final, multi-objective scenario incorporates each of the three cost functions into a single run.

Table 5: Multi-Objective Genetic Algorithm Settings

Setting Value Algorithm Non-Dominated Sorting Genetic Algorithm III Seeds and Repetition Random Seed (Mersenne Twister) Termination Criteria Perform 1,000,000 Function Evaluations Initial Population 500 Crossover Probability 0.5 Mutation Probability 0.5 Mutation Rate 0.02 Mutation Sigma 0.1 An understanding of the general operation of the model and the underlying cost functions will help to contextualize results. A brief discussions of the limitations of this method is included with Conclusions in Chapter Five.

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CHAPTER 4: RESULTS

4.1 MODEL PARAMETERS

In the following chapter, data will be provided for four scenarios. These scenarios consist of three single-objective genetic algorithm runs and one multi-objective genetic algorithm run. The three single-objective problems represent one each of Economic,

Environmental, and Social Performance. The final multi-objective run represents all three variables considered simultaneously.

For the single-objective runs, a standard genetic algorithm is used. Due to the number of initial solutions that will be unsuitable due to having too great or too small of a floor area, a large initial population size of 500 is utilized. The model uses tournament selection with a tournament size of ten. The model relies on mutation to avoid early convergence, selecting the

“correct” winner of the tournament 100% of the time. An aggressive crossover probability of

0.7 is used to ensure a breadth of novel solutions. Both ‘Jump’ and ‘Creep’ mutations are utilized with a probability of 0.1 and 0.75 respectively, allowing mutations to both change solutions incrementally and in more substantive ways. This should allow for searching of

“nearby” solutions while also prevent convergence at a relative minimum rather than a global minimum. The model is set to stop after 50,000 function evaluations, or approximately 141s generations.

For the multi-objective run, a NSGA-III Algorithm is selected. The robust initial population of 500 members is once again utilized. Crossover probability and mutation probability are set to 0.5, meaning that in each successive generation, one half of the population will consist of crossovers of the best solutions from the previous generation and one half of the total population will receive some mutation. The mutation rate is 0.02, meaning that two percent of each “mutants” “genes” will be changed. Sigma, which controls the mutation

72 magnitude, is set to 0.1. The model is set to run for the number of total calculations corresponding to 100 generations.

Results are centered around four scenarios. Scenario A through C are single-objective scenarios evaluating optimum performance for economic, environmental, and social cost functions respectively. Scenario D is a multi-objective scenario evaluating all three cost functions simultaneously. The results for each of the four scenarios is preceded by some pre- scenario information. This consists of additional information of interest about individual buildings rather than neighborhood scale implementation. This latter information was gleaned from the model without any application of optimization algorithms.

4.2 PRE-SCENARIO INFORMATION

Even prior to the execution of any optimization, it is possible to use a more “brute force” approach to interrogate the model for certain questions about individual buildings. For example, while the model will search for a combination of buildings and heights which will best fulfill the given scenario, it may be of interest to investigate the relationships between individual buildings and their height to better understand the more complex scenarios. Three such questions are answered below:

4.2.1 Relationship Between Building Height and Profit.

The first pre-scenario evaluates the economic portion of the model to ascertain at what height buildings generate the most profit on a per square footage basis. This question balances the rent or sale price premium for height vs. the reduced floor plate efficiency and increased costs associated with building taller buildings. A summary of profitability per building for heights at ten floor increments is provided in Figure 16 below.

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Profit/SF by Height for Five Building Types $300.00

$200.00

$100.00

Apartment $‐ Condo Affordable $(100.00) Office Hotel $(200.00)

$(300.00)

$(400.00) 10 20 30 40 50 60 70 80 90 100

Figure 16: Per Unit Floor Area Profit per Building by Type and Height

As can be seen in this figure, it appears Apartment and Condominium Buildings have the tallest “optimum height” for the model as presently constructed. This logically follows the parameters on which the model is based, where apartment rent and condominium sale price consider height premiums. That is, apartments and condominiums on taller floors are more valuable, which tends to offset the increase in price for taller buildings to some degree. A similar effect is visible in Office Prices, where some rent premium is commanded, but the premium is less as a percentage of construction costs than that of the market-rate residential uses. Premium for height is not considered in the model for hotel rooms or affordable housing, as the relationship is unclear in the former case and in the latter case, rent is based entirely on income and does not vary by floor height. The model appears to suggest an optimal profitability for market-rate residential projects between 30 and 50 floors in height. Certainly, within Chicago, there has been no shortage of such construction. However, the model also suggests that shorter Offices and Hotels are preferable from a profit perspective. This notion is

74 less supported by recent building, which has included numerous hotel and office projects of heights greater than ten stories.

4.2.2 Relationship Between Building Height and Return on Investment

Of course, the cost function by which economic outcomes are evaluated is not simple profit, but rather return on investment. Profit alone does not consider how much money must be invested to access economic gains. Certainly an investor would rather invest ten dollars for a two dollar return rather than investing one hundred dollars for a three dollar return, despite the fact that net profit is greater in the second case.

Figure 17 shows the relationship between return on investment and height for each building type. Once again, this accounts for the premium rents and for-sale prices paid for higher floors, which is weighed against the greater cost and reduced efficiency of such building.

ROI by Height for Five Building Types 100.0%

50.0%

Apartment 0.0% Condo Affordable ‐50.0% Office Hotel

‐100.0%

‐150.0% 10 20 30 40 50 60 70 80 90 100

Figure 17: Return On Investment by Building Type and Height

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When one considers the increased amount of money which must be paid “up front” to construct such tall buildings, the profitability advantage previously observed in market rate residential projects of greater height appears to evaporate entirely. While greater net profit may be made on a market-rate apartment tower of forty floors than twenty floors on a per-unit-floor- area basis, the initial investment in the forty-story building is so much higher, it outweighs the modest gains in profitability expected.

Were the above information 100% true, it is difficult to envision a world in which any high-rise would ever be built for profit motive. This suggests that some of the variables driving high-rise construction are not totally accounted for in the model. Such variables which are not considered may include land cost, the cost of infrastructure development, and the prestige and brand awareness associated with taller buildings.

4.2.3 Relationship Between Greenhouse Gas Emissions and Building Height

Just as the model makes it possible to evaluate individual buildings economic performance based on height, so to is it possible to evaluate economic performance. The net greenhouse gas emissions produced by each building type and height are show in Figure 18.

This figure accounts for the embodied, operational, and transportation energy as explained in the previous chapter.

As one may expect, greenhouse gas emissions appear to show a linear relationship with building height. As transportation, embodied, and operational energy were evaluated on a per-unit-floor area basis, it stands to reason that the greater the floor area, the greater the measurement of such emissions. A more interesting way to evaluate the relationship between height and environmental performance may be on a per-unit-floor area basis. Figure 19 provides such an evaluation.

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Lifecycle GHG Emissions by Building Height and Type 7000000

6000000

5000000

4000000

3000000

2000000

1000000 TotalLifecycle GHG (MT C02E)

0 10 20 30 40 50 60 70 80 90 100 Building Height

Apartment Condo Affordable Office Hotel

Figure 18: Total Greenhouse Gas Emissions by Building Type and Height

Lifecycle GHG Emissions per Net SF by Building Height and Type 6

5

4

3

2

1 Lifecycle GHG Emissions per Net SF (MT C02E) 0 10 20 30 40 50 60 70 80 90 100 Building Height

Apartment Condo Affordable Office Hotel

Figure 19: Per Unit Floor Area Greenhouse Gas Emissions by Building Type and Height

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Figure 19 appears to show that even accounting for the obvious fact that buildings with greater floor area under roof will use greater amounts of energy, height still comes at a premium in terms of energy consumption. This stands to reason, given that the taller the building, the less efficient the floor plate. Spaces for hallways and vertical circulation must still be heated, cooled, and illuminated. Thus, the fact that more such spaces are included in taller structure makes the environmental performance worse on such building per unit of rentable floor space. Note that each residential modality conforms so closely that their representative lines “cover up” one another.

This simple investigation is not intended to imply that taller buildings are intrinsically less sustainable (although that may be the case). Rather, it is intended to illuminate what the model does not consider. For example, the construction of tall buildings may make the city denser, allowing for more efficient transportation networks and reducing the embodied energy of infrastructure. Any such gains are not captured in the model and may provide fertile ground for further investigation.

4.3 SCENARIO A: ECONOMIC OPTIMUM

The first scenario in which the use of an evolutionary algorithm is undertaken involves a single-objective optimization for economic performance. The parameters for both model and cost function are explained in Chapter 3 above. The average and best fitness of the solution pool is shown in Figure 20. The performance of the “best ever” solution for each generation in shown in Figure 21. This represents only the portion of the algorithm which was necessary to find the best solution, plus ten generations for context. In reality, the model was allowed to run for several more generations after convergence had occurred to see if any creep or jump mutations might slightly improve the solution. In this run, the best solution was found in generation 107.

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Economic Performance by Generation 0.600 0.400 0.200 0.000 -0.200 -0.400 -0.600 -0.800 -1.000

Rate of Return (Higher is Better) -1.200 -1.400 1 4 7 10 13 16 19 22 25 28 31 34 37 40 43 46 49 52 55 58 61 64 67 70 73 76 79 82 85 88 91 94 97 100 103 106 Generation

Best Average

Figure 20: Best and Average Solutions by Generation for Single-Objective Economic Model

Best Economic Solution 0.550

0.500

0.450

0.400

0.350

0.300

Rate of Return (Higher is Better) 0.250

0.200 1 4 7 10 13 16 19 22 25 28 31 34 37 40 43 46 49 52 55 58 61 64 67 70 73 76 79 82 85 88 91 94 97 100 103 106 Generation

Figure 21: Best Solution by Generation for Single-Objective Economic Model

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As shown in the figures above, while the solutions generated as part of the initial population were not terribly fit, the model seems to have worked correctly in selecting fitter and fitter outcomes, without converging prematurely on the preferred solution. The optimum solution had a cost function value of 0.503. Since the optimum solution is between 12,500,000 square feet and 13,500,000 square feet, the cost function is not penalized and thus its value of

0.503 corresponds to a return on investment of 50.3%. The “optimum” solution selected is shown in Figure 22, where one bar represents one building and the height of each cluster of bars represents the optimal building height.

Economic Factors Only 100

90

80

70

60

50

40 Height (Floors) 30

20

10

0 Apartment Condo Affordable Office Hotel Building Use

Figure 22: Optimum Solution for Single-Objective Economic Model

Perhaps unsurprisingly, the optimum economic solution contains no affordable housing, as the rent for such housing is considerably lower than other building modalities. The inclusion of Hotel and Office buildings of lesser height is also unsurprising, given that lower heights are where returns are maximized for these typologies where rent is less height sensitive or not height sensitive at all. That the apartment buildings are taller is also explicable as a

80 matter of return. As shown in Figure 22, while Hotel and Office building return is maximized at lower heights, this is less true for apartment buildings, which are economical to build taller due to the rent premiums commanded for taller units. A single condominium building is included, but this is likely only because the model built near the maximum ten apartment buildings and adding height to these buildings would harm their economic prospects due to increasing costs and lowering efficiencies. Since the model seeks to reach 13,000,000 square feet, a single condominium building is added.

4.4 SCENARIO B: ENVIRONMENTAL OPTIMUM

The second scenario considered is the single-objective optimization for environmental performance. As explained in Chapter 3, environmental performance in this case means the reduction of greenhouse gas to the lowest levels allowable while maintaining a square footage in the desired range. Figure 23 shows the average and best solutions through the generations which were necessary to find the best solution (plus ten generations for context). Figure 24 shows the evolution of the best solution alone over this timeframe. Solutions are measured in lifecycle tons of carbon-dioxide equivalent emissions.

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Environmental Performance by Generation 160,000,000

140,000,000

120,000,000

100,000,000

80,000,000

60,000,000

40,000,000

20,000,000

0

LIfecycle GHG Emissions (Lower is Better) 1 2 3 4 5 6 7 8 9 101112131415161718192021222324252627 Generation

Best Average

Figure 23: Best and Average Solutions by Generation for Single-Objective Environmental Model

Best Environmental Solution 16,000,000

15,500,000

15,000,000

14,500,000

14,000,000

13,500,000 Lifecycle GHG Emissions (Lower is Better) 13,000,000 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 Axis Title

Figure 24: Best Solution by Generation for Single-Objective Environmental Model

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Unlike the Economic Model, which took 97 generations to find the best outcome, the best outcome found by the Environmental Model was located in only the 17th generation and was not improved upon in subsequent generations. It is unclear if this is due to a more well calibrated penalty function or if it simply shows luck in the pool of initial solutions generated. In any case, the lowest greenhouse gas emissions by a qualifying model were found to be

13,394,795 MT of CO2E. This optimum solution is shown in Figure 25.

Environmental Factors Only 100

90

80

70

60

50

40 Height (Floors) 30

20

10

0 Apartment Condominium Affordable Office Hotel Building Use

Figure 25: Optimum Solution for Single-Objective Environmental Model

Once again, the result here is somewhat unsurprising given what is known about the model. As Figure 25 above shows, residential uses have the lowest per-unit-floor area lifecycle energy consumption when compared to office and hotel uses. The preference for

Condominium over Apartment and Affordable Residences is explained by the larger footprint of the units in each condominium. In these buildings, there are fewer persons per square foot, less the transportation emissions, which are associated with the number of people in the building, are reduced. Since condominiums offer the mix of low operational energy and fewer

83 traveling occupants, their square footage is almost maximized. Since this model is not concerned with the costs associated with greater height, much taller buildings are selected.

The embodied energy of taller condominium buildings appears to be overwhelmed by their other “energy saving” characteristic, as seen by the model.

Market-rate apartments and Affordable Housing should be somewhat interchangeable in terms of energy use associated, so the models preference for the former appears to be a matter of chance. The inclusion of a single, minimum height office building is also unexpected, given the energy use intensity of these buildings. It is possible that the model here has found a very good solution, but that a solution without ten stories of office building is actually the solution with the overall minimum cost function result.

4.5 SCENARIO C: SOCIAL OPTIMUM

The third scenario executed is a single-objective case which looks for the optimum configuration resulting in the highest “social score,” as defined in Chapter 3 above. Figure 26 shows the average and best solutions over the course of the generations necessary to find optimum. Figure 27 shows only the best outcome over the course of the subject generations.

As with the other single-objective models, ten additional generations are included for context.

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Social Performance by Generation 0.500

0.300

0.100

-0.100

-0.300

-0.500

-0.700 135791113151719212325272931333537394143

Social Sustainabiltiy Factor (Higher is Better) Sustainabiltiy Social Generation

Best Average

Figure 26: Best and Average Solutions for Single-Objective Social Model

Best Social Solution 0.360

0.355

0.350

0.345

0.340

0.335

0.330

0.325

Social Sustainability Factor (Higher is Better) Sustainability Social 0.320 1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 39 41 43 Generation

Figure 27: Best Solution for Single-Objective Social Model

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In this model, the number of generations necessary to find the best solution located is somewhere between the possible “lucky guess” of the Environmental Model and the more laborious process of the Economic Model. The best social score was found in the 34th generation of the algorithm run shown above. This solution had a cost function value of 0.354, corresponding to a social score of 35.4%. This solution is shown in Figure 28.

Figure 28: Optimum Solution for Single-Objective Social Model

Given the focus of the Social Score on providing Affordable Housing, it is unsurprising that the majority of buildings in this configuration are Affordable Apartments. Given the importance of jobs, the inclusion of offices, which are the most job-intensive of the modalities considered, also stands to reason. The provision of a single, very tall apartment tower is a little bit more of a mystery.

Apartment buildings have tended to have the highest net present value, thus generation the highest taxes. Yet, because taxation is based on income net of costs and expenses, it

86 seems as though several shorter apartment buildings would have performed better. Given the declining efficiency of taller buildings, it is possible the model is trying to generation as few market-rate units as possible in an effort to avoid reducing the percentage of housing that is affordable. Since the model considers the percentage of total housing units that are affordable rather than the absolute number of units, an aversion to construction of market-rate units would be expected.

The fact that this model does not appear averse to tall buildings is unsurprising, as it does not consider the cost of generating such buildings. The inefficiency of taller building floor plates may be the only factor limiting the model from selecting even fewer even taller buildings.

4.6 SCENARIO D: MULTI-OBJECTIVE OPTIMUMS

The final scenario involves the simultaneous optimization for all three variables: economic, environmental, and social, as explained in Chapter 3 above. From the population size of 500 individuals, generation 100 offers 161 novel, first-rank solutions. This means that as of this generation, 161 solutions are non-dominated and can not be improved for any one objective without harming their performance on another objective.

The data are difficult to visualize in a single manner, as each represents multiple axes.

With that said, it may make sense to consider each variable individually, then consider the relationship between two variables, and finally visualize all three variables simultaneously. To consider each variable individually, Figure 29 provides a histogram for the 161 novel solutions according to their evaluation by each of the three cost functions. These data have not been normalized, but the environmental performance has been reduced by 10-7 to allow for legibility.

Each set of solutions is split into even deciles.

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Figure 29: Histograms showing Distributions of First Rank Solutions by Objective Cost Function

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Generally, the model appears to have found non-dominated solutions which maximize economic performance, as the first histogram above shows a distribution weighted towards high return on investment. The same can be said of environmental performance, with the carbon impacts of many of the solutions minimized. The least skewed distribution belongs to the social objective, where the highest social scores were achieved in relatively few solutions relative to higher economic and lower environmental scores.

This phenomenon may suggest that it is more difficult to harmonize social objectives with economic and environmental objectives, which are more easily harmonized with one another. If one normalizes the entire data set to values between one and zero (with one representing the best outcome and zero the worst for each cost function), a cursory look at the data suggests that this may be correct. Of the 161 solutions, the highest performing social solution has the worst performance for both economic and environmental objectives. This was not true for the best economic and environmental scoring outcomes. The best economic outcome in the data set had moderate environmental and social performance. Likewise, the best environmental outcome in the dataset has poor economic performance but fairly strong social performance.

This relationship can be seen more clearly by examining the variables two-at-a-time to identify the correlation between the best performing solutions. Figure 30 shows the relationship between normalized environmental and economic scores. Figure 31 shows the relationship between normalized economic and social scores. Figure 32 shows the relationship between environmental and social scores.

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1st Rank Solutions: Economic vs. Environmental 100%

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0 - 20% Social 21 - 40% Social 41 - 60% Social 61 - 80% Social 81 - 100% Social

Figure 30: Non-Dominated Solution Set by Economic and Environmental Performance

Evaluation of the solution set on only economic and environmental variables shows that the variables may be somewhat correlated, but that the correlation does not appear terribly strong. While few solutions have poor scores in both economic and environmental performance, solutions with strong performance in one category may have only moderate performance or even weak performance in another. Given the right-weighted distributions seen in the histograms above, the trade-off between environmental and economic performance does not appear as straightforward as the other comparisons below. Some solutions were able to perform well in both economic and environmental score.

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1st Rank Solutions: Economic vs. Social 100%

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0 - 20% Env. 21 - 40% Env. 41 - 60% Env. 61 - 80% Env. 81 - 100% Env.

Figure 31: Non-Dominated Solution Set by Economic and Social Performance

A much stronger correlation is shown between economic and social scores. Solutions with strong economic performance tend to have poor social performance. Likewise, solutions with strong social performance tend to have poor economic performance. The absence of any data points in the upper right of the graph suggests that no solution was able to harmonize economic and social performance terribly well. This suggests that the trade-offs between economic and social performance are more direct that the trade-offs between economic and environmental performance.

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1st Rank Solutions: Environmental vs. Social 100%

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0 - 20% Econ 21 - 40% Econ 41 - 60% Econ 61 - 80% Econ 81 - 100% Econ

Figure 32: Non-Dominated Solution Set by Environmental and Social Performance

The relationship between social and environmental performance appears to be neither as strongly correlated as that between economic and social performance nor as loosely correlated as that between economic and environmental performance. While the bareness of the lower left quadrant in the graph does suggest that few solutions had poor performance on both categories, some solutions appear to have performed fairly well on both categories. This would suggest that environmental and social performance are not mutually exclusive.

However, some trade-off between environmental and social performance is observed.

Finally, it is possible to visualize all three performance criteria at once. Figure 33 generates a plane for economic and environmental performance from which prisms are projected upwards. The height of these prisms represents social performance.

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Figure 33: Normalized Economic, Environmental, and Social Performance of Non-Dominated Solutions

The solutions appear to form a “surface” as would be expected for a Pareto-optimal set.

Observations drawn from examining any two variables at once appear to be reinforced by seeing this whole point cloud/surface simultaneously. First, economic and environmental performance do not appear mutually exclusive, with many solutions performing well on both economic and environmental grounds. Second, economic and environmental performance appear to have some trade-off, but this did not prevent some environmentally strong solutions from having adequate-to-good social performance. Third, economic and social performance appear to have the strongest negative correlation, with good performance in both categories appearing to be mutually exclusive.

With an understanding of which variables appear to affect one another most closely, the largest question remaining is what the individual solutions look like. Aggregated information about the solutions can help to draw some conclusions regarding the way in which the model was trending. Such population information is provided in Table 6.

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Table 6: Population Information for First Rank Solutions

Apart. Condo Afford. Office Hotel Apart. Condo Afford. Office Hotel Number Number Number Number Number Height Height Height Height Height Minimum 1 1 0 0 04635282019 Maximum 9 8 8 5 39772535942 Average 7.78 4.23 2.76 2.04 0.04 80.94 45.65 38.61 25.39 36.23 Median 8 4 2 2 08545392436 StdDv 1.15 1.83 1.99 1.36 0.33 13.22 6.43 5.67 5.00 3.52

The non-dominated solutions appear to have selected from a wide range of possible building number combinations, especially for residential projects, which show the greatest degree of variation between total building counts. For offices and hotels, the variation is less, with no solution opting for more than five offices or three hotels. The average number of hotels is also noteworthy. While the range of outcomes was between zero and three, the average of only 0.04 suggests that most solutions had no hotel use at all. Hotels may be hampered by providing relatively poor environmental performance and relatively few jobs. Offices have stronger employment numbers, but their weak environmental and economic performance likely contributed to their exclusion as a major part of many solutions.

By number, the highest average and median belong to apartments, then condominiums, and finally affordable housing. Apartments offer strong economic performance and ecological performance that is better than offices or hotels. Condominiums are a slightly weaker economic prospect but have superior environmental performance due to their lower number of occupants. Affordable housing was likely selected due solely to its impact on social variables.

In terms of building height, the tallest building of any solution was a 97-story apartment building. Generally, apartments had the highest average and median heights, possibly because of rent premium paid for heights in residential projects. It is also worth noting that market-rate residential had the highest standard deviation, suggesting a wide-spread between the tallness of these buildings between non-dominated solutions. Affordable housing tended to

94 be lesser height, likely because rents are not responsive to taller units. Hotels and offices built only in the most economic (i.e. least expensive) cost ranges.

Of course, information about the population is insufficiently granular to understand any given solution. It may also be of interest to examine several “middle of the road” solutions to see their composition. For this exercise, three solutions were selected. These scenarios are not necessarily preferable to any other scenarios. Aside from meeting the criteria outlined below, their selection from the pool of non-dominated solutions is a matter of chance. In all three solutions, no objective had a normalized score of less than 0.42. Solution A had

Economic, Social, and Environmental scores of 0.60, 0.42, and 0.74 respectively. Solution B had scores of 0.79, 0.52, and 0.53. Finally, Solution C had scores of 0.51, 0.84, and 0.54.

Each solution managed strong performance of at least 0.74 in its strongest category. Those strengths were Environmental for Solution A, Economic for Solution B, and Social for Solution

C. The solutions are shown in Figures 34, 35, and 36 respectively. These are not intended to be an exhaustive view of the solution space, but only to represent how three points, selected with some empirical criteria but otherwise randomly, “look” in terms of model outcomes.

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Each solution is emblematic of the greater solution set in several ways. First, no solution relies on hotels, which were rarely used within the body of solutions. Second, the tallest buildings are residential, with the tallest among those being apartments. Third, offices are used in varying degrees but are not as tall as residential buildings. The number and height of residences likely owes to their economic strength and rent premiums for height. The offices have a lower premium for height, and thus are typically built in a more cost effective range.

At least for these “middle-of-the-road” solutions, buildings have a fairly substantial variety in type within each solution. The number of buildings between solutions is somewhat consistent, with the greatest difference being the inclusion of more Offices in Solution C.

Between solutions, the heights of each building type are remarkably uniform.

Of course, this represents only three of the 161 possible non-dominated solutions from the 100th generation of the model run. More variety may be captured by selecting other

97 solution candidates, especially if less care was given to ensuring the selected candidates performed at least moderately well on each of the three objectives.

4.7 SCENARIO VISUALIZATIONS

A simple number and type of buildings, while of importance to the programming and design process, is not in and of itself a design. To contextualize one proposed solution and demonstrate how such information may be realized on the actual site in question, so visualizations have been conducted. Figures 37 and 38 show two such visualizations.

Figure 37: View of Scenario A from south along Wells/Wentworth Connector

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Figure 38: View of Scenario A from northwest along the Chicago River

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CHAPTER 5: CONCLUSIONS

The course of this investigation began with identifying a problem: the lack of balance in the present architectural discourse and profession. It continued by examining one empirical method of resolving the kind of competing objectives faced by architects: the multi-variate evolutionary algorithm. It then proposed a specific, neighborhood-scale problem and a method by which such an algorithm might by applied. Next, it explored the results produced by the algorithm. Finally, it must offer some evaluation of the success or failure of this endeavor.

5.1 OUTCOME EVALUATION

Just as we began with the Planners Triangle as guide to sustainable balance in Chapter

1, we may now return to this diagrammatic notion as a means of evaluating the method put forward. Figure 45 shows the Planner’s Triangle updated with the single-variable optimization solutions envisioned in Scenarios A through C and the multi-variable optimization solutions reviewed in Scenario D. While this is not intended to be a literal placement of where each solution might graphically fall, it does offer a means of understanding what each solution to the problem considered has sought to do.

While the changing nature and economic realities of architectural practice may have forced the architect to place a disproportionate amount of importance on profitability, the method identified in this investigation offers one possible solution as to how the architect’s objectives may be realigned. By quantifying the desired objectives, understanding the trade- offs between those objectives, and modeling a means of empirically balancing competing demands, this investigation has sought to demonstrate that balance itself is possible with the right tools.

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This investigation was applied to a new scale, considering more than a building but less than a city. This scale was attacked through the coopting of the mega development, which offers the opportunity to make neighborhood-scale decisions in a relatively narrow timeframe.

The proposed method seems suitable to the scale selected, given the limitations discussed below.

Figure 39: Optimization Solutions on the Planner's Triangle

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5.2 LIMITATIONS

The proposed method unfortunately does not offer a panacea for all that ails architecture. It is, after all, only as good as the information which is used to make the underlying decisions. Should the variables change from the number and height of buildings to the orientation or massing of buildings, it would be difficult to make such a mid-course correction. Likewise, if rents should drop from (for example) $50.00 per square foot to $45.00 per square foot, the utility of the model may very well be compromised. Clearly defined inputs and means of evaluation appear to be required for this method. It seems as though, in the day- to-day trenches of architectural practice, such clear-cut problems may rarely present themselves.

The method also relies on a willingness to use empirical means to solve problems.

Even without the encumbrance of complex algorithms, it seems that many design decisions made today do not use the best information available. Even where progress has been made towards solving design problems, this progress seems to be often overlooked in pursuit of more familiar ways of building.

The narrowness of the problem considered also qualifies as a limitation of this method.

While a neighborhood-scale development has untold millions of design decisions which must be made, here focus was placed only on the limited problem of building use, quantity, and height. It is likely that an actual design would require the addition of many more variables, which may cause the model to become more convoluted. While the ability to consider multiple competing objectives is this methods strength, its complexity relative to the application of more normative theory may be a weakness, at least as far as implementation is concerned.

The “black box” nature of algorithmic design may also be a limitation in a field in which assigning a reason to one’s decision-making is highly valued. While the conventional design

102 process allows for projects to be traced through numerous intermittent steps as design goals change and evolve, this method produces solutions somewhat opaquely.

Finally, unlike single-variable optimization, this multi-variable method intrinsically produces a multitude of solutions. While the problem considered herein was comprised of discrete, bounded variables and thus had a finite number of solutions in the Pareto Set, design problems with less constrained or continuous variables will offer a theoretically infinite number of solutions. Therefore, this method can be used only to “narrow the field” of possible solutions. Selecting from among the solutions identified as appropriate remains a design problem which is outside the scope of this investigation.

It is also worth noting that the diverse body of solutions proposed likely have impacts to which the model is totally agnostic. Consider that one solution may propose fifteen buildings while the other proposes sixteen. If both solutions emit an equal amount of carbon, the model will express no environmental preference. Yet, it may very well be that the elimination of an entire building footprint allows for some other important environmental goal, such as the inclusion of on-site power generation or greater carbon sequestration in on-site plantings.

Because the methods employed are intentionally novel, little considering has been given to previous work regarding form-based codes, solar zoning codes, and other evaluations of tall building massing impacts on cityscapes. A more complex model may be able to incorporate some of the body of research which evaluates outcomes based on tall building massing, but as presently constructed, the model is not sufficiently detailed to incorporate this information.

5.3 APPLICABILITY TO DESIGNERS

A survey of the methodology of this investigation may imply that the analysis things more in “developer terms” than “architectural terms.” After all, the questions asked, how many

103 buildings and what height, are often conceived of in the development and programming process and may or may not be within the purview of the architect to change.

However, the polemical exercise of balancing the competing objectives is very architectural. While the literal questions of height may be a forgone conclusion due to zoning or programming requirements, the idea of evaluating solutions not by aesthetic whims or by performance in a singular criterion appears to have merit for architectural design. The greatest value of this investigation may be less about the explicit answers to the questions and more about the method in which the questions are posed.

5.4 FURTHER RESEARCH

While the author has some modest hope that preceding pages have contained some answers to difficult questions, it is undoubtable that just as many or more new questions have been raised as a result of this exercise. Around every corner in which one decision was made, another decision could have yielded a different result, potentially radically changing outcomes.

First, while evidence is offered to suggest that the architect’s goals have become somewhat misaligned, scarce research exists on the change in architectural priorities over time. If the discipline really is changing for the worse, it is likely insufficient to evaluate such a change anecdotally. More research is required to understand the magnitude of such a change as well as its social, economic, and political progenitors.

The investigation relied on the concept of premium for height in cost, structural material, and rent. Yet, while the concept of premium for height has existed at least as long as the high- rise itself, the specific relationships between height and variables have been poorly defined.

More research is necessary to better understand the trade-offs between tall and small in order to understand the trade-offs between competing objectives.

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The external validity of the method in question also requires further investigation. While a single problem was selected here, it is unclear if the model that was built could be used to solve different problems or if each novel problem would necessitate the building of an entirely new model. In practice, it seems as though “short cuts” would have to be developed to simplify the model building process by incorporating variables and objectives common to many projects.

More research is also needed in understanding the longitudinal variability in these design problems. While timeline of construction was omitted for simplicity, the time of construction and occupation likely has tremendous effects on the cost of construction and other financial outcomes. In a broader sense, neighborhoods are not built overnight. It is entirely within the realm of possibility that The 78, the example analyzed in this investigation, may take multiple decades to build. Over the course of such a time period, objectives and even variables may change substantially as new concerns become more important and old modalities fade.

The model does not address “weighting” of variables as presently constructed. That is to say, each outcome is treated as equally important and the algorithm is agnostic to outcomes that perform better one on one variable than the other. It may be true that this is rarely the case in design practice, where certain variables are often more important than others. Other prospective variables, such as life safety, may not be suitable for compromise at all. While these “make or break” variables may be more accurately treated as constraints, the exercise of assigning weights may be instructive. Further research would be necessary to explore how the model would operate under different weighting scenarios, and which weighting scenarios would be most applicable to design practice.

Finally, while this model sought to predict outcomes for design objectives for given solutions, in reality any such exercise would need to be validated with post-occupancy review.

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Whether the solutions that our computers deem optimal actually performed optimally remains somewhat of an open question, not only in regards to this investigation, but more broadly in the field of computational design and construction.

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