CIFE
Center for Integrated Facility Engineering
CIFE Seed Proposal Summary Page
2014-15 Projects
Proposal Title: Enhancing Decision-Making on Sustainable Building
Projects Using Influence Diagrams
Principal Investigator(s): Michael Lepech, Ross Shachter (MS&E)
Research Staff: Kelcie Abraham
Proposal Number: (Assigned by CIFE): 2014-05
Abstract (up to 150 words):
Architecture, engineering, and construction teams need more effective decisionmaking methods and visualization tools, particularly during early-stage design and
pre-construction of sustainability-focused projects when decisions have the most
impact on sustainability performance. The multi-disciplinary nature of these
decisions and the engagement of multiple stakeholders often result in decision
problems with multiple objectives and confusing alternatives, necessitating
decision-making approaches that clearly represent the rich data environment
owners and AEC professionals face. This work proposes an approach based on
influence diagrams. While influence diagrams have been used in other industries
(e.g. environmental management) with great success, they have seldom been
applied to the built environment. This research will evaluate the ability of decisionmaking visualization methods to enhance sustainability objectives. Ultimately, by
better integrating visualization methods into decision-making and developing
more rigorous techniques that are quick and easy to use, AEC professionals can
help clients achieve sustainability goals with greater efficiency and clarity.
1. Motivation
1.1. Research Background
Across the U.S., demand for environmentally sustainable buildings is growing; as
shown in Figure 1, the market for non-residential green buildings has increased
rapidly over the past decade. Green buildings are “structures…that are
environmentally responsible and resource-efficient throughout the building’s life
cycle” (US EPA 2012). Often, green buildings are certified through programs like
the US Green Building Council’s Leadership in Environmental and Energy Design
(LEED) rating system, which envisions green buildings as structures that reduce
operating costs and increase asset value; decrease waste sent to landfills;
conserve energy and water; are healthier and safer for occupants; and reduce
harmful greenhouse gas emissions (USGBC 2012). As of June 2012, two billion
square feet of building space have been LEED-certified, and LEED is referenced
in project specifications for 71% of projects valued at $50 million and over
(USGBC 2012). In 2010, 78% of architecture and engineering firms and 81% of
contractors reported that client demand was driving them toward green building
investment (MHC 2010). Consequently, professionals in the Architecture,
Engineering, and Construction (AEC) industry need to become skilled at
developing and delivering green buildings for clients.
Non-Residential Green Building Market Size (US)
$122 billion
48% of
market
$3 billion
2% of market
2005
$58 billion
41% of
$47 billion
market
31% of
$25 billion market
12% of
market
2007
2009
2011
2013
2015
Figure 1 The value of the non-residential green building market in the US has grown significantly
since 2005; the share of new construction starts that are green has also increased. Adapted from
McGraw Hill Construction (2012)
Over the course of a building project, architecture, engineering, and construction
(AEC) consultants predict and evaluate the performance of many different design
and construction options. The daily design-construction recommendations that
these AEC consultants make to the client have significant impacts on the
sustainability of the building throughout its life cycle. (Ugwu and Haupt 2007) With
contractors and designers frequently working under strict budget and schedule
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constraints, AEC consultants need superior planning, design, and construction
processes to achieve client goals – and the tools to communicate these
processes and the options they consider in a clear, effective fashion. It is also
critical that AEC professionals have the appropriate tools and effective
visualization tools to explain sustainability options and pathways to owners and
clients that have limited knowledge and experience with sustainability issues and
the ways by which sustainable built environments can be constructed.
One of the most active and important stages for decision-making and choice
representation to owners and clients is during the design development and
construction documents stages of the building lifecycle. This means that during
design, decision makers are dealing with decisions that range from a relatively
large number of options (and thus can be hard to explain to owners) to those that
have fewer, better-defined alternatives to consider (but require a more detailed
analysis and thus can be equally hard to explain to owners). Thus, the ability to
model and visually represent the decision-making processes associated with
sustainable construction is similarly useful across throughout the design
development timeline even as the ability of decision makers to change project
design and cost decreases and the cost of design changes (in terms of resources
and budget) increases significantly. The MacLeamy Curve, displayed in Figure 2,
describes this phenomenon.
Figure 2 The MacLeamy Curve (MSA-IPD 2012)
1.2. Industry Example
AEC professionals and researchers are actively investigating formal decision
processes in practice, primarily for developmental design and pre-construction. A
case study was conducted with a CIFE member company during the preconstruction phase of a single large-scale, sustainability-focused project,
executed using Integrated Project Delivery (IPD). The company sought clear
communication and desired a rigorous decision-making process to ensure that
high-level performance goals, including cost, schedule, safety, and sustainability
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objectives, would be met. The multi-objective, multi-disciplinary nature of
sustainability decisions meant that visualizing and selecting an appropriate
decision-making process was key. The team was concerned with identifying an
appropriate process for project-related decisions that had a significant impact on
project cost or schedule and required supporting information from AEC
professionals. These decisions included, but were not limited to, trade partner
selection, technology adoption, and detailed systems comparisons.
For several months during pre-construction, the company implemented two
formal, value-based decision methods: first Weight, Rate, and Calculate (WRC),
then Choosing By Advantages (CBA) (Suhr 1999). However, the company found
that neither process led to decisions of satisfactory quality and eventually rejected
both. In general, decision makers found that CBA enabled multi-disciplinary
stakeholder participation and added value to decision-making for simple decision
problems. However, decision makers also believed that CBA was inefficient and
ineffective for more complex decision problems and did not adequately clarify
decision rationale since it was overly difficult to communicate the results and
options in clear and understandable fashion. The introduction of an online tool,
Wecision Enterprise (DPI 2013), to support CBA improved efficacy, efficiency,
and value of information derived from the decision-making process, but clarity of
rationale and choice remained an issue due to the inherent complexity of decision
problems and inconsistencies in factor selection between decisions. These
observations suggest the need for future research concerning the design and
implementation of appropriate tools for visualization of decision-making processes
and options on lean or sustainability-focused projects.
1.3. Research Objectives
This research has two primary goals. The first is to determine how influence
diagram visualization tools can support decision-making methods that produce
the highest quality decisions – that is, how well are they suited for complex
decision-making on sustainability-focused projects. The second objective is to
evaluate the potential for using influence diagram techniques to integrate and
communicate sustainability objectives within design and construction decisionmaking and in turn, to establish a foundation for further research to determine
how integrated sustainability decision-making can be enhanced through
visualization of complex decision processes.
2. Points of Departure
This proposed research is based on three separate points of departure. The first
point of departure is multi-criteria decision analysis (MCDA). MCDA provides a
rigorous decision-making structure that is amenable to rigorous visualization of
the decision-making process, trade-offs among various objectives, and decision
quality evaluation metrics. The second point of departure is decision-mapping
through the use of influence diagrams. Influence diagrams provide a rigorous
visualization structure amenable to the complex, multi-objective decisions that are
made on sustainability-focused projects. The third point of departure is integration
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with sustainability science and the metrics associated with sustainable built
environments. Together these three points of departure form the foundation of this
proposed research program.
2.1. Multi-Criteria Decision Analysis (MCDA)
The multidisciplinary nature of AEC decision-making and the engagement of
multiple stakeholders often result in decision problems with multiple objectives.
This particular structure of decision-making calls for a set of approaches referred
to as multi-criteria decision-analysis (MCDA). MCDA methods structure and
model the imprecise goals of multi-dimensional decision problems in terms of a
set of individual decision criteria, where each criterion characterizes a single
dimension of the problem to be evaluated. The general framework for most MCDA
involves decomposing the decision problem into components, evaluating each
component individually, and reassembling the components to provide overall
insights and recommendations (Seppälä et al. 2002).
AEC professionals and researchers are actively investigating formal decision
processes in practice, primarily for early-stage design and pre-construction to
select projects for investment (Lam et al. 2001; Dey 2006), choose project
procurement methods (Kumaraswamy and Dissanayaka 2001; Anderson and
Oyetunji 2003; Mahdi and Alreshaid 2005), and enhance early-stage design
(Ugwu and Haupt 2007; Turskis et al. 2009; Flager et al. 2012). The majority of
MCDA methods examined in the literature and implemented in practice are valuebased. Value or utility theory approaches ask decision makers to develop a
numerical score or value for each decision alternative and choose the alternative
with the highest value. Examples include multi-attribute utility theory (MAUT),
analytic hierarchy process (AHP), and simple multi-attribute rating techniques with
swing weights (SMARTS) (Lahdelma et al. 2000). CBA is also considered a
value-based method. In the AEC industry, CBA has been applied to decisions
about green roof systems (Grant and Jones 2008), installation of viscous damping
walls (Nguyen et al. 2009), and exterior wall assemblies (Arroyo et al. 2012,
2013).
Although a number of decision-making techniques are currently being
investigated and applied throughout the design and construction process, few
researchers have studied pre-construction applications of value-based methods
or the implementation of non-value-based methods on sustainable building
projects. Current work at CIFE is examining the usefulness of Pareto fronts as a
visualization tool to represent performance trade-offs between conceptual design
options in sustainable building designs (Abraham et al. 2014). These studies have
found that when decision makers were supplied with decision-making
visualization tools alone (i.e. Pareto fronts), they found it difficult to interpret tradeoffs constructively and distinguish differences in quality between designs.
Within ongoing CIFE programs, charrette-based design teams were given a
design challenge to produce a building design that minimizes life cycle impact
while also considering first cost and construction schedule. Participant teams
were given varying levels of decision support. The first group was given no formal
decision-making tools and no visualization tools to support their decision-making
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process (i.e. Pareto fronts). The second group was given no formal decisionmaking tools but was provided Pareto fronts that showed the tradeoff among
various design alternatives. The third and fourth groups were provided formal
MCDA tools both without and with Pareto visualization tools, respectively. As
seen in Figure 3, design solution quality iteratively increased most rapidly when
providing both decision-making tools and visualization tools. However,
visualization tools alone (without formal decision-making processes) can be very
confusing and result in a degradation of decision-making ability to the point where
incorrect conclusions can be made.
Figure 3. Iterative Improvement in Solution Quality by Decision Support Tool
Fundamentally, when provided unfamiliar information about sustainabilityfocused design decisions through visualization tools it is difficult for decisionmakers to evaluate its impact without being shown how this information fits within
the overall decision-making process. As such, the development of the right
visualization tools to accompany decision-making frameworks is critical. To
provide this type of visualization, influence diagrams have been applied in a
number of fields, including environmental management and energy facility siting,
as a way of demystifying the "black box" - complex multi-objective decisionmaking models - that generates the Pareto fronts being used to effectively guide
sustainability-focused decision-making.
2.2. Influence Diagrams for Decision-Making
Influence diagrams have been introduced and used in a number of complex
decision-making scenarios (Shachter 1986). The theory of influence diagrams
dates back to the early 1980s, and a variety of commercial software packages are
on the market. An influence diagram is an expansion of a decision tree: a
graphical structure with directed arcs and with decision, chance and expected
value nodes. Within these diagrams, hierarchical structure is not required, only
that directed cycles of decision-making are not permitted. The approach is both
straightforward and practical in structuring and constructing models, in performing
optimization, sensitivity tests, and value of information analysis, along with
analyzing management problems under various prevailing risk models. A sample
influence diagram for early-stage decision-making for building siting and massing
is shown in Figure 4. Within this diagram, building program, gross floor area, and
building location are initial modeling inputs and the influence of later decisions
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and variables include construction sequencing decisions, material supply chain
decisions, and weather variables.
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Used in environmental management (Varis et al. 1990; Varis 1997; Kuikka et al.
1999), siting of hazardous waste management facilities (Merkhofer et al. 1997),
and more recently energy plant siting (Zhou et al. 2006) influence diagramming
allows probabilistic, Bayesian studies with classical decision analytic concepts
such as risk attitude analysis, value of information and control, multi-attribute
analysis, and various structural analyses. Such diagrams can serve as a crucial
link between higher-level decision-making visualization tools (i.e. Pareto fronts)
and the operational decisions that owners and AEC professionals are faced with
on a regular basis.
2.3. Sustainability Integration
Decisions and decision-making processes associated with sustainability-focused
construction are expected to be significantly different from the decisions and
decision-making processes of traditional construction. This is due in large part to
the often-conflicting nature of sustainability objectives, the relative unfamiliarity of
building owners and AEC professionals with many sustainability concepts and
metrics, and the extended validation process and efficacy timeline associated with
most sustainability efforts. As such, highly useful visualization tools of the
decision-making process are needed.
The achievement of sustainability goals can only be known far into the future.
Comello et al. (2012) note that sustainable development and construction is now
defined by the criteria used to recognize it: for example, “pre-construction air
quality permit requirements,” “purchase of renewable energy,” “attainment of
LEED certification.” As discussed by Comello, these are not correct formal logic
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Visualization of Built Environment Decision-Making
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definitions. A major problem with such concepts of sustainable construction is the
fundamental ex post facto nature of sustainability: in most categories, today’s
construction projects may only be deemed sustainable many years from now.
Defining sustainability in such long time frames with little certainty in feedback
means there is little incentive to focus attention on sustainable practices due to
the high level of uncertainty in the returns on such sustainability investments. The
absence of definite feedback visualization in the short-term – positive or negative
– significantly complicates the design and construction decision-making process.
3. Methods
3.1. Charrette Tests
Decision-making charrettes similar to those conducted by Clevenger and
Haymaker (2011), Senescu and Haymaker (2013), and Abraham (2014) will be
conducted with Stanford students and subsequently, industry professionals from
interested CIFE companies. Influence diagrams will be developed in consultation
with two or more individuals from CIFE companies to develop as complete and
accurate a representation of the decision problem as possible. These individuals
will not participate in the charrette but will provide feedback to the researchers on
the quality of the influence diagram framework.
The first charrette will be composed of two mock decision scenarios and will be
used to test the effect of implementing influence diagram visualizations. The
decision scenarios will reflect common situations in sustainability-focused design
that are easy to understand and simple to evaluate, with approximately 50-60
independent design alternatives. The first proposed decision problem revolves
around balancing life-cycle cost with initial construction cost of a project. Working
with Beck Technologies (CIFE member), a framework for quantifying these
parametric trade-offs has been developed and a populated design space can be
constructed. The second problem is balancing life-cycle impact with initial
construction cost and project duration. Once again, this charrette will be
supported by Beck and additional software developed at Stanford by Basbagill et
al. (2013). The decision scenarios will include all performance prediction data
relevant to the problem and an explicit statement of client project objectives. A
number of charrette groups will analyze one decision scenario without either the
support of an influence diagram or Pareto front. This analysis will constitute the
business-as-usual case. Subsequently, the groups will analyze a similar decision
problem with the aid of influence diagrams and Pareto fronts. Time and resources
permitting, the group will analyze the second decision scenario using only one of
these two resources. The use of two decision scenarios is meant to minimize the
learning effect from solving the decision problem multiple times.
The groups will conduct their analyses using Microsoft Excel templates
provided by the researchers. These Excel templates will contain macros to track
the group’s progress throughout the analysis, including a complete history of cell
value assignments, and compile the relevant data, which are identified in the next
section. Following each decision scenario, participants will be asked to complete
short group and individual surveys (4-5 questions) related to the process. This is
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meant to elicit decision maker satisfaction with process, visualization, and
outcome. The information gathered from the charrettes will support preliminary
evaluation and comparison of the decision methods and streamlining of the
charrette for testing with industry groups. For the industry charrette tests, the
decision scenarios may also be modified to reflect a broader set of sustainabilityfocused design decisions on the project.
3.2. Comparing Decision-Making Visualization Methods
The decision visualization methods will be tested against several metrics adapted
from the literature for selecting a decision-making approach. The effectiveness of
visualization techniques will be evaluated using data from the charrettes as well
as verbal feedback from the participants when relevant. Metrics will include:
1. Stakeholder participation
2. Clarity of rationale
3. Efficiency of process
4. Effectiveness of process
5. Value of information delivered
Comparison metrics have been identified from a study conducted by Davey
and Olson (1998), who compared two different group decision support methods
and one commercial group decision support system. Descriptive variables to be
determined from the Excel spreadsheets include number of preference changes,
number of solutions changed, time to solution, preference changes per unit time,
and solutions considered per unit time. An example of this type of metric is shown
in Figure 5 (Abraham et al., 2014). Within Figure 5 the normalized design quality
is plotted as a function of design iteration for various design teams using differing
levels of data visualization (Pareto fronts or not) and formal decision-making
processes (MCDA or not).
Figure 5. Design Solution Quality versus Design Iteration Demonstrating the Quantification of
Performance Metrics
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Davey and Olson (1998) also suggest user satisfaction as comparative metric;
this will be assessed qualitatively through the surveys and verbal feedback.
3.3. Assessing Sustainability Integration
As discussed previously, evaluating the achievement of sustainability objectives in
the short term poses an interesting challenge. One possible short-term evaluation
measure for visualization of decision-making is the satisfaction of the design
team, client, or owner. Having specified a desired level of “green” or sustainability
performance for the overall project during preliminary design, most design teams
see their achievement of these goals take shape in the design and preconstruction phases. As such, advanced specification and careful selection of
sustainability decision criteria is a major component of the enhanced visualization
framework with practical implications. Understanding how visualization of
sustainability-focused decision-making processes influences the success or
perceived success of design team sustainability objectives can provide insight into
how likely team members are to implement these visualization tools within a
rigorous decision framework. In many regards, notions of client and design team
satisfaction are a practical measurement of achieving sustainability goals for a
construction project, particularly in light of the ex post nature of sustainability, and
can relate directly to repeat work for the contracting firms and economic viability
of the project team. This satisfaction will be measured though feedback surveys
and interviews with charrette participants.
4. Relationship to CIFE Goals
4.1. Schedule Performance
This research will enable the visualization of complex, sustainability-focused
decision-making processes that require fewer process iterations and result in
higher client and design team satisfaction with decision process and quality. Thus,
it allows teams to generate better performance predictions and conduct further
analyses as opposed to spending time in decision-related meetings.
4.2. Cost Conformance
While the use of influence diagrams is focused on sustainable design decisionmaking, these same tools have wider applicability to all project design objectives
– including cost. Thus, this research will help designers and owners visualize the
relationship between explicitly stated project development objectives and
decisions that determine project performance. The improvements to visualizing
decision-making methods will also allows decision stakeholders to more clearly
observe and monitor that the intended process “checklist” has been followed
successfully, and may also lead to improvements in value-for-money.
4.3. Sustainability
The proposed research will lead to additional design team members and owners
understanding and participating in the decision-making processes that will help
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decision makers to explicitly and systematically consider sustainability objectives
in decision-making. As quantitative sustainability metrics become more widely
used, decision-makers will be more able visualize, communicate and incorporate
their knowledge easily into complex decision analysis without altering their
decision-making process.
4.4. Globalization
As sustainability goals are implemented for projects all around the world, it is
necessary for competitive construction firms to have flexible, understandable,
clear, yet robust decision-making methods in place to efficiently and quickly make
decisions at every stage of a sustainability-focused project. The proposed
research will lead to improvements in construction firms communicating,
visualizing, and understanding the design process for projects that are considered
to be highly sustainable designs. Much of this understanding is now lost during
inefficient and confusing decision-making efforts.
5. Industry Involvement
As noted in examples from Abraham et al., (2014) and Basbagill et al, (2014),
CIFE members and other industry partners have been involved in past projects
and will continue to be heavily involved in this project through the development of
beta-phase visualization tools and participation in the charrettes. Further testing of
the enhanced visualization tools may be pursued on different CIFE member
industry projects, and additional follow-up interviews and surveys may be
conducted with industry participants.
This work also leverages the industry experience and relationships of MS&E
Professor, Ross Shachter, as a Co-PI. Professor Shachter is a recognized expert
in the field of influence diagrams in support of complex decision-making with a
great deal of experience in connecting academic research to industry practice.
The proposed research will be Professor Shachter’s first research effort with
CIFE.
6. Research Plan, Schedule, and Risks
6.1. Research Plan
1. Student Charrette (May/June 2014): development of Excel templates (user
interface and macros and visualizations), implementation of charrette tests
at Stanford.
2. EPOS Conference (July 2014): presentation of initial observations from
sustainable campus project and general framework for evaluating decision
methods in conference proceedings (abstract accepted; full draft in review),
presentation of more detailed evaluation framework and preliminary
findings from student charrettes.
3. Industry Charrette (August/September 2014): modification of charrettes to
address visualization of real project decisions, implementation with CIFE
members and industry partners.
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4. Charrette Follow-Up (October/November 2014): continued charrette testing
to collect any remaining data on sustainability integration and follow-up
with industry participants.
5. Journal Publications (January-March 2015): publications on (1) the
evaluation and comparison of visualization methods and (2) enhancing
sustainability integration submitted to peer-reviewed journals in
construction management, sustainable construction, or decision analysis.
6.2. Risks
1. Software Development – To implement the decision-making software for
use by CIFE industry partners, an easy-to-use graphical user interface
(GUI) will need to be developed for the visualization tool. This risk is
mitigated by leveraging an existing Excel-based GUI that was developed
for a separate research effort focusing on parametric analysis of
sustainability decision variables.
2. Timeline - The project timeline may be difficult to meet if industry projects
cannot be found in the near-term for study this upcoming summer. Projects
must be found that are in the design or pre-construction stage and have
aggressive sustainability goals. This risk will be mitigated by offering
participation to as many CIFE members as possible to find projects that are
in the correct stage of planning and construction.
3. Project Identification - Researchers are dependent on the participation of
CIFE’s industry partners to validate the proposed visualization method.
Industry collaborators must be willing to provide a set of case study
decisions that focus on sustainability goals along with access to
researchers to observe the decision-making process, implement charrettes
with participants, and follow up with inquiries. This risk will be mitigated by
offering participation to as many CIFE members as possible to find the
right size and type of sustainability-focused project.
7. Next Steps
Following CIFE seed funding, additional funds will be sought through a proposal
to the National Science Foundation (Directorate of Engineering) to be authored by
the PI that focuses on the application and extension of visualization techniques to
sustainability-focused decision-making beyond the construction industry.
Regardless of additional funding support, this work will continue through 2015
based on leveraged funding from the Stanford Graduate Fellowship program.
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