VIETNAM NATIONAL UNIVERSITY HO CHI MINH CITY
HO CHI MINH CITY UNIVERSITY OF TECHNOLOGY

DO DUY LINH

COMBINING BUILDING INFORMATION MODELING (BIM)
AND CHOOSING BY ADVANTAGES (CBA) METHOD TO
SELECT DESIGN-CONSTRUCTION SOLUTIONS TOWARD
SUSTAINABLE CONSTRUCTION IN VIETNAM

Major: CONSTRUCTION MANAGEMENT
Major code: 8580302

MASTER’S THESIS

HO CHI MINH CITY, July 2023


THIS THESIS IS COMPLETED AT
HO CHI MINH CITY UNIVERSITY OF TECHNOLOGY – VNU-HCM
Supervisor: Assoc. Prof. Luong Duc Long

Examiner 1: Assoc. Prof. Tran Duc Hoc

Examiner 2: Dr. Chu Viet Cuong

This master’s thesis is defended at HCM City University of Technology,VNUHCM City on 13th July 2023
Master’s Thesis Committee:
1. Dr. Nguyen Anh Thu

- Chairman

2. Dr. Huynh Nhat Minh

- Secretary

3. Assoc. Prof. Tran Duc Hoc

- Reviewer 1

4. Dr. Chu Viet Cuong

- Reviewer 2

5. Dr. Dang Ngoc Chau

- Member

Approval of the Chairman of Master’s Thesis Committee and Dean of Faculty
of Civil Engineering after the thesis being corrected (If any).
CHAIRMAN OF THESIS COMMITTEE

HEAD OF FACULTY
OF CIVIL ENGINEERING

Dr. Nguyen Anh Thu


i
VIETNAM NATIONAL UNIVERSITY - HO CHI MINH CITY
HO CHI MINH CITY UNIVERSITY OF TECHNOLOGY


SOCIALIST REPUBLIC OF VIETNAM
Independence – Freedom - Happiness

THE TASK SHEET OF MASTER’S THESIS
Full name: Do Duy Linh
2170311
Date of birth: 21/10/1993
Major: Construction Management

Student ID:
Place of birth: Khanh Hoa
Major ID: 8580302

I. THESIS TITLE (In Vietnamese): Kết hợp mơ hình thông tin xây dựng (BIM) và
phương pháp lựa chọn theo ưu điểm (CBA) để lựa chọn các giải pháp thiết kế và thi
công hướng đến xây dựng bền vững tại Việt Nam.
II. THESIS TITLE (In English): Combining Building Information Modeling (BIM)
and Choosing By Advantages (CBA) method to select Design-Construction solutions
toward sustainable construction in Vietnam.
III. TASKS AND CONTENTS: Building a predictive model of energy consumption for
buildings using Building Information Modeling (BIM), Building Energy Modeling
(BEM) and Machine Learning; Determining the factors affecting the decision to
choose the design and construction option towards sustainable construction and the
importance level of those factors; Developing a method for choosing optimal design
and construction options by Choosing By Advantages (CBA) method.
IV. THESIS START DAY: March 2023
V. THESIS COMPLETION DAY: 10th June 2023
VI. SUPERVISOR: Assoc. Prof. Luong Duc Long
Ho Chi Minh City, date ………

SUPERVISOR
(Full name and signature)

HEAD OF DEPARTMENT
(Full name and signature)

Assoc. Prof. Luong Duc Long
DEAN OF FACULTY OF CIVIL ENGINEERING
(Full name and signature)

Note: Student must pin this task sheet as the first page of the Master’s Thesis booklet


ii

ACKNOWLEDGEMENTS
In order to complete this thesis, first of all, I would like to express my sincerest
thanks to Associate Professor, Dr. Luong Duc Long, who has enthusiastically guided,
oriented and imparted valuable experiences to me during the process of making this
thesis.
Next, I would like to thank the teachers of the Department of Construction
Management, Faculty of Civil Engineering for their dedication in teaching and
imparting specialized knowledge during their study at the school.
I also would like to thank the group of experts, colleagues and friends who have
given their comments, participated in surveys as well as shared valuable knowledge and
experiences and supported me in the process. perform this research.
Finally, I would like to thank my family and relatives for always supporting and
encouraging me during my study and thesis completion.
In the process of conducting research, errors cannot be avoided. Therefore, I look
forward to receiving your understanding and comments to improve.

Sincerely,
Ho Chi Minh City, 31st July 2023

DO DUY LINH


iii

ABSTRACT
Energy efficiency buildings are becoming more and more important in order to
address the current energy problem and advance in line with the sustainable trend of the
construction industry. The potential for energy savings in buildings is relatively large
when design-construction options are used to increase energy efficiency. This has many
tangible advantages for the socio-economy, including increased energy efficiency,
improved quality of life, and a favorable effect on the environment. Finding a designconstruction alternative for the building that is truly beneficial in terms of both technical
and economic criteria, however, is proving to be very challenging. The goal of this thesis
is to help solve this issue.
To accomplish this task, Building Information Modeling (BIM) and software in this
ecosystem are not only used for modeling (3D), time simulation (4D), quantity
measurement (5D) but also for energy consumption simulation (6D). This thesis focuses
on using BIM-based energy simulation software to simulate energy consumption for
various types of building design and combining BIM 6D with the Choosing by
Advantages (CBA) method, a multi-criteria decision-making method that has been
widely applied in selecting design options, materials, and contractors related to
sustainable construction. In addition, besides using energy software to simulate and
generate a data set of energy consumption for the building, this thesis introduces a
method to predict energy consumption by using Learning Machine based on output
datasets from the generating process.
Keywords: Building Information Modeling (BIM), BIM 6D, Choosing by Advantages
(CBA), sustainable construction, Machine Learning



iv

TĨM TẮT LUẬN VĂN THẠC SĨ
Các tịa nhà sử dụng năng lượng hiệu quả ngày càng trở nên quan trọng hơn nhằm
giải quyết vấn đề năng lượng hiện nay và tiến tới phù hợp với xu hướng phát triển bền
vững của ngành xây dựng. Tiềm năng tiết kiệm năng lượng trong các tòa nhà là tương
đối lớn khi các phương án thiết kế-xây dựng được sử dụng để tăng hiệu quả sử dụng
năng lượng. Điều này mang lại nhiều lợi ích hữu hình cho nền kinh tế xã hội, bao gồm
tăng hiệu quả sử dụng năng lượng, cải thiện chất lượng cuộc sống và tác động tích cực
đến mơi trường. Tuy nhiên, việc tìm kiếm một phương án thiết kế-xây dựng cho tịa nhà
thực sự có lợi về cả tiêu chí kỹ thuật và kinh tế đang tỏ ra rất khó khăn. Mục tiêu của
luận án này là giúp giải quyết vấn đề này.
Để thực hiện nhiệm vụ này, Mô hình thơng tin xây dựng (BIM) và phần mềm trong hệ
sinh thái này không chỉ được sử dụng để mô hình hóa (3D), mơ phỏng thời gian (4D),
đo lường khối lượng (5D) mà cịn dùng để mơ phỏng tiêu thụ năng lượng (6D). Luận
văn này tập trung vào việc sử dụng phần mềm mô phỏng năng lượng trên nền BIM để
mô phỏng mức tiêu thụ năng lượng cho các loại thiết kế cơng trình và kết hợp BIM 6D
với phương pháp Lựa chọn theo Ưu điểm (CBA), một phương pháp ra quyết định đa
tiêu chí đã được áp dụng rộng rãi trong việc lựa chọn thiết kế, vật liệu và nhà thầu liên
quan đến xây dựng bền vững. Ngoài ra, bên cạnh việc sử dụng phần mềm năng lượng
để mô phỏng và tạo bộ dữ liệu tiêu thụ năng lượng cho tòa nhà, luận văn này giới thiệu
phương pháp dự báo mức tiêu thụ năng lượng bằng cách sử dụng Learning Machine dựa
trên bộ dữ liệu đầu ra từ quá trình mơ phỏng năng lượng.
Từ khóa: Building Information Modeling (BIM), BIM 6D, Choosing by Advantages
(CBA), sustainable construction, Machine Learning


v


THE COMMITMENT OF THE THESIS’ AUTHOR

The undersigned below:
Student full name:

DO DUY LINH

Student ID:

2170311

Place and date of born:

Khanh Hoa, Vietnam, 21st October 1993

Address:

Di An City, Binh Duong

With this declaration, the author finishes his master’s thesis entitled
“COMBINING

BUILDING

INFORMATION

MODELING

(BIM)


AND

CHOOSING BY ADVANTAGES (CBA) METHOD TO SELECT DESIGNCONSTRUCTION SOLUTIONS TOWARD SUSTAINABLE CONSTRUCTION
IN VIETNAM” under the advisor's supervision. All works, ideas, and materials that
was gain from other references have been cited correctly.

Ho Chi Minh City, 31st July 2023

DO DUY LINH


vi

TABLE OF CONTENTS
CHAPTER 1: GENERAL INTRODUCTION ..................................................................... 1
1.1.

Research problem: ............................................................................................. 1

1.2.

Objectives of the topic ....................................................................................... 1

1.3.

Scope of study: ................................................................................................... 2

1.4.


Scientific and practical significances ................................................................. 2

1.4.1.

Practical significances ........................................................................................ 2

1.4.2.

Scientific significances ....................................................................................... 2

CHAPTER 2: THEORETICAL BASIC AND RELATED RESEARCH ......................... 3
2.1.

Definitions and concepts .................................................................................... 3

2.1.1.

Sustainable Development ................................................................................... 3

2.1.2.

Energy-Efficient building ................................................................................... 4

2.1.3.

Building Information Modeling (BIM) .............................................................. 4

2.1.4.

Multiple-criteria decision-making (MCDM) methods: ...................................... 5


2.1.5.

Choosing by Advantages (CBA) methods: ........................................................ 6

2.1.6.

Artificial Intelligence and Machine Learning .................................................. 11

2.2.

Relative research .............................................................................................. 14

CHAPTER 3: METHODOLOGY ....................................................................................... 18
3.1.

Research procedure .......................................................................................... 18

3.2.

Collecting data ................................................................................................. 20

3.3.

Data Analysis Tools ......................................................................................... 22

3.4.

Energy simulation software: DesignBuilder .................................................... 25


3.5.

Random Forest Algorithm ............................................................................... 26

3.5.1.

General ............................................................................................................. 26

3.5.2.

The process of building a Random Forest model ............................................. 27


vii

3.5.3.

The advantages of Random Forest Algorithm ................................................. 27

3.5.4.

Evaluate the accuracy of the RF model ............................................................ 29

3.6.

Building a model to predict energy consumption ............................................ 30

3.7.

Software used in the study ............................................................................... 30


CHAPTER 4: DETERMINING FACTORS - DATA COLLECTION AND
ANALYSIS ............................................................................................................................. 32
4.1.

Determining the factors.................................................................................... 32

4.2.

Questionnaire Design ....................................................................................... 33

4.3.

Survey results ................................................................................................... 33

4.4.

Analyzing the characteristics of the study sample ........................................... 33

4.5.

Testing the reliability of the scale .................................................................... 36

4.6.

Ranking factors ................................................................................................ 37

4.7.

One sample T-Test ........................................................................................... 38


4.8.

Multi-sample testing ........................................................................................ 39

CHAPTER 5: BUILDING A MODEL OF ENERGY CONSUMPTION ...................... 46
5.1.

Simulation of building energy consumption using DesignBuilder .................. 46

5.1.1.

Procedure of building an energy simulation model ......................................... 46

5.1.2.

Model information ............................................................................................ 47

5.1.3.

Defining design variables and input data to DesignBuilder ............................. 47

5.1.4.

Simulation results: ............................................................................................ 53

5.2.

Simulation of building energy consumption using Random Forest Algorithm


by Python Programming Language. .............................................................................. 54
5.2.1.

Procedure of creating an energy prediction model........................................... 54

5.2.2.

Data and parameter for RF model .................................................................... 56

5.2.3.

Checking results of prediction model ............................................................... 57

5.3.

Optimal design variables.................................................................................. 60


viii

5.3.1.

Pareto frontier ................................................................................................... 60

5.3.2.

Pareto frontier result from DesignBuilder ........................................................ 60

5.3.3.


Pareto frontier result from Prediction model and comparison ......................... 61

CHAPTER 6: DECISION ON DESIGN OPTIONS BY CBA METHOD ..................... 63
6.1.

CBA procedure and result ................................................................................ 63

6.1.1.

Identifying alternatives ..................................................................................... 63

6.1.2.

Defining factors ................................................................................................ 63

6.1.3.

Defining criteria for each factor ....................................................................... 64

6.1.4.

Describing the attributes of each alternative .................................................... 65

6.1.5.

Deciding advantages of alternatives & Decide the importance of advantages 73

6.1.6.

Evaluating cost data .......................................................................................... 75


6.2.

Finding the most optimal alternative by pareto frontier .................................. 80

6.3.

Method of using aggregate values without units of measurement to rank

alternatives ..................................................................................................................... 81
CHAPTER 7: CONCLUSION AND SUGGESTION ...................................................... 84
7.1.

Conclusion ....................................................................................................... 84

7.2.

Suggestion ........................................................................................................ 85

REFERENCE ......................................................................................................................... 87


ix

TABLE INDEX
Table 2.1: Summary of some previous relative researches .........................................14
Table 3.1: Statistics on the amount of data collected ..................................................22
Table 3.2: Meaning of Cronbach’s Alpha coefficient values ......................................23
Table 3.3: List of software used in the study .............................................................. 31
Table 4.1: Factors affecting to decision of choosing design-construction options .....32

Table 4.2: Percentage of participants who have ever joined in Green Building
projects or energy efficient buildings ............................................................................33
Table 4.3: Years of experience of the survey participants ..........................................34
Table 4.4: Expertise of the survey participants ........................................................... 34
Table 4.5: Roles of the survey participants .................................................................35
Table 4.6: 1st results of reliability testing ...................................................................36
Table 4.7: 2nd results of reliability testing ..................................................................36
Table 4.8: Ranking factors through Mean values........................................................37
Table 4.9: Results of One-sample T-Test ..................................................................338
Table 4.10: Mean difference analysis for the experience of the respondents ...............39
Table 4.11: Mean difference analysis for the expertise of the respondents ..................41
Table 4.12: Mean difference analysis for the role of the respondents ..........................43
Table 5.1: Define design variables ............................................................................447
Table 5.2: Design variables data .................................................................................48
Table 5.3: Simulation results in DesignBuilder ..........................................................53
Table 5.4: Procedure of creating a prediction model for energy consumption ...........54
Table 5.5: Comparison of electricity consumption results generated by Prediction
model and DesignBuilder ............................................................................................557
Table 5.6: Comparison of Discomfort hour results generated by Prediction model and
DesignBuilder ..............................................................................................................558


x

Table 5.7: Comparison of CO2 emission results generated by Prediction model and
DesignBuilder ................................................................................................................59
Table 5.8: Pareto frontier result from DesignBuilder..................................................60
Table 5.9: Pareto frontier result from Prediction model .............................................61
Table 5.10: Duplicated pareto variable sets between simulation by DesignBuilder and
Prediction model ............................................................................................................62

Table 6.1: Optimal Alternatives ..................................................................................63
Table 6.2: Factors and their importance score ............................................................ 63
Table 6.3: Factors and their criteria .............................................................................64
Table 6.4: Properties of materials ................................................................................66
Table 6.5: Points of materials ......................................................................................70
Table 6.6: Attributes of alternatives ............................................................................71
Table 6.7: CBA assessment .........................................................................................73
Table 6.8: Material unit rates.......................................................................................75
Table 6.9: Cost of Alternatives ....................................................................................76
Table 6.10: Evaluation summary ...................................................................................80
Table 6.11: Detail evaluation of Method of using aggregate values without units of
measurement ..................................................................................................................83


xi

FIGURE INDEX
Figure 2.1: CBA Steps ...................................................................................................10
Figure 2.2: Working process with Machine Learning ...................................................12
Figure 3.1: Research procedure .....................................................................................18
Figure 3.2: Data collecting procedure ...........................................................................20
Figure 3.3: One-way ANOVA analyzing process .........................................................24
Figure 3.4: Process of building a Random Forest model ..............................................27
Figure 3.5: Procedure of building a model to predict energy consumption ..................30
Figure 5.1: Procedure of building an energy simulation model in DesignBuilder .......46
Figure 5.2: Input weather data .....................................................................................447
Figure 5.3: Design variables setting in DesignBuilder..................................................52
Figure 5.4: Objectives and Outputs setting ...................................................................52
Figure 5.5: Example of a Pareto front ...........................................................................60
Figure 5.6: Pareto frontier result from DesignBuilder ..................................................61

Figure 5.7: Pareto frontier result from Prediction model ..............................................62
Figure 6.1: CBA Score comparison...............................................................................74
Figure 6.2: CBA Score and Cost Comparison .............................................................. 80


xii

LIST OF ABBREVIATION
AAC: Autoclaved Aerated Cement
Adv: Advantages
AEC: Architecture, Engineering, and Construction
AHP: Analytic Hierarchy Process
AI: Artificial Intelligence
Alt: Alternative
BIM: Building Information Modeling
BEM: Building Energy Model
BO: Building Orientation
CBA: Choosing by advantages
CIB: Council for Research and Innovation in Building and Construction
CP/CoP: Coefficient of Performance
CST: Cooling Setting Point
DCH: Discomfort Hour
GB: Green Building
Imp: Importance
IofAs: Importance of Advantages
MCDM: Multiple-criteria decision-making
ML: Machine Learning
NSE: Nash Sutcliffe Efficiency
PSO: Particle Swarm Optimization
QS: Quantity Surveyor

RF: Random Forest
SHGC: Solar Heat Gain Coefficient
WCR: Weighting Rating and Calculating
WW: Window on Wall


1

CHAPTER 1: GENERAL INTRODUCTION
1.1.

Research problem
Currently, climate change, global warming, together with the shortage of

resources and energy, are becoming significant challenges for humanity in general and
the construction industry in particular. The fast-growing population and high
urbanization rates increase the demand for housing worldwide, especially in developing
countries like Vietnam. The scale of construction projects is expanding both in terms of
size and height to meet the residential and working needs. This makes the construction
industry one of the largest consumers of energy and resources and greenhouse gas
emissions.
Traditionally, the design, materials, and construction methods have focused more on
aesthetics, functionality, and cost than other factors. This leads to uncontrolled energy
consumption and CO2 emissions during the construction and operation of structures.
However, along with high energy consumption, the potential for energy savings for
buildings is also significant. According to research by the International Energy Agency,
buildings are estimated to account for about 41% of global energy savings by 2035.
Therefore, if appropriate measures are taken to improve energy efficiency, it will
significantly reduce the long-term operation costs of the project, reduce CO2 emissions,
bring economic benefits, and improve the environment and quality of life.

The urgent task posed for investors, project managers, and designers is to find design
solutions that not only meet the requirements of functionality, aesthetics, and cost but
also optimize the energy used to operate the project and minimize CO2 emissions. This
is to bring about sustainable development in the long-term future for the construction
industry.
1.2.

Objectives of the topic
The objective of this study is to:

-

Building a predictive model of energy consumption for buildings using Building
Information Modeling (BIM), Building Energy Modeling (BEM) and Machine
Learning.


2

-

Determining the factors affecting the decision to choose the design and construction
option towards sustainable construction and the importance levels of those factors.

-

Developing a method for choosing design and construction options by Choosing By
Advantages (CBA) method.

1.3.


Scope of study:
The scope of this study includes:

-

Type of project: housing, office, apartment projects.

-

Object of study: BIM-based BEM in energy consumption simulation, the factors
affecting the design selection decision related to sustainable construction and
”Choosing by Advantages” method, Machine Learning.

-

Object of survey: Experts in Sustainable construction field from Investors, Project
managers, Contractors, Engineers.

1.4.

Scientific and practical significances

1.4.1. Practical significances
Helping design consultants and stakeholders determine the influence of building
design features on energy consumption, thereby selecting the most optimal solutions to
apply to improve energy consumption and the ability to use energy efficiently for the
building.
1.4.2. Scientific significances
Propose a model to predict energy consumption based on design characteristics

of buildings.
The research results will become a reference base for future studies on energy efficient
design solutions for buildings in Vietnam.


3

CHAPTER 2: THEORETICAL BASIC AND RELATED RESEARCH
2.1.

Definitions and concepts

2.1.1. Sustainable Development
In 1987, the United Nations World Commission on Environment and
Development released the report Our Common Future, commonly called the Brundtland
Report. The report included a definition of "sustainable development" which is now
widely used: Sustainable development is development that meets the needs of the
present without compromising the ability of future generations to meet their own needs.
It encompasses economic, social, and environmental aspects and seeks to promote longterm development that is both equitable and environmentally sound. Sustainable
development involves addressing issues such as poverty, inequality, climate change,
biodiversity loss, and resource depletion, and aims to create a world where people can
live prosperous lives without exceeding the carrying capacity of the planet. It requires
an integrated and holistic approach to decision-making that takes into account the
interdependencies between economic, social, and environmental factors.
Among the biggest challenges to sustainable development, the construction industry
plays a fundamental role. During the Final Session of the First International Conference
of CIB TG 16 on Sustainable Construction in 1994, Professor Charles J. Kibert provided
the following definition of sustainable construction: “the creation and responsible
management of a healthy built environment based on resource efficient and ecological
principles”. Notably, the traditional concerns in construction (performance, quality,

cost) are replaced in sustainable construction by resource depletion, environmental
degradation and healthy environment [1]. Sustainable construction addresses these
criteria through the following principles set by the conference: [1]
-

Minimize resource consumption (Conserve)

-

Maximize resource reuse (Reuse)

-

Use renewable or recyclable resources (Renew/Recycle)

-

Protect the natural environment (Protect Nature)

-

Create a healthy, non-toxic environment (Non-Toxics)

-

Pursue quality in creating the built environment (Quality)


4


Additional definitions and frameworks for sustainable construction practices were more
rigorously defined in the 1999 Agenda 21 on Sustainable Construction, published by the
International Council for Research and Innovation in Building and Construction (CIB)
[2]. In order to correct biases evident in the initial report as a result of the majority of
contributors coming from the developed world, the same council also released a second
edition of the agenda for sustainable construction in poor nations in 2001 [2].
2.1.2. Energy-Efficient building
An energy efficient building offers an appropriate environment for habitation
with minimal energy consumption and wastage of energy, thereby maximizing energy
conservation [3].
An energy-efficient building ensures that residents have a comfortable living
environment while using the fewest resources and the least amount of energy. Measures
to make a building energy-efficient encompass the building’s entire lifecycle: the
construction process itself, going into the operation maintenance cycle and demolition
phases of the building [3]. An energy-efficient structure retains full operation and
thermal comfort for its residents.
An energy-efficient structure establishes a balance between all aspects of energy
utilization in a building by providing an ideal blend of energy-efficient machinery,
renewable energy sources, and passive solar design strategies.
2.1.3. Building Information Modeling (BIM)
When referring to the graphical representation of a building, the terms 2D and
3D are often used. In fact, BIM includes many other aspects (or dimensions) that add
useful information to the project to be executed or managed as follows: 1D, 2D, 3D
Information 3D Graphics; 4D - Timeline & Scheduling Information; 5D - Cost
information and analysis; 6D - Sustainability and energy efficiency; 7D - Facilities
management information; and so on [4].
6D BIM adds sustainability information to a building's geometric model, aiming to
accurately estimate energy usage early on and reduce consumption over the building's
life cycle. This allows for research into energy-efficient alternatives and the



5

incorporation of sustainable energy sources. Specific software is required to create the
model, known as a Building Energy Model (BEM).
Building Information Modeling (BIM) has proven to be an effective tool for envelope
design and construction, particularly when it comes to selecting the appropriate type of
glass. By using BIM, designers and architects can analyze a building's solar exposure
and thermal performance, as well as identify potential clashes between envelope
elements and other building systems. According to a study by Chu [5] "BIM modeling
can help optimize energy-saving solutions for building facade design." The study found
that by using BIM, designers can analyze a building's energy performance and select
glass types that provide optimal energy efficiency. Ghiassi and Zhang [6] found that
BIM simulation tools can evaluate different glass types, coatings, and thicknesses to
determine their impact on energy efficiency. This leads to selecting the most energyefficient materials.
In summary, the use of BIM in envelope design allows for informed decisions regarding
the selection of glass and other envelope materials, leading to more efficient and
sustainable building design.
2.1.4. Multiple-criteria decision-making (MCDM) methods:
Every day, businesses make a variety of decisions, including those involving the
hiring of personnel, the choice of technologies, the layout of their operations, etc. It
seems sensible to assume that various decisions will necessitate various decisionmaking processes. Roy (1974) categorized various decision-making processes. The
summaries of these are as follows:
-

Describing: a description of each possibility and its key effects

-

Sorting: dividing up all of the options into groups or categories.


-

Ranking: creating a ranking of all acceptable options.

-

Choosing: selecting the best option among all the alternatives (or a combination of
them).

There are also other types of decisions described later by Belton and Stewart (2002):
- Selecting a Portfolio: to select a subset of options from a wider collection of available
options.


6

- Designing: to do research, find, or develop new decision alternatives to satisfy the
objectives and desires disclosed by the decision-making process.
These kinds of decisions can be supported by several approaches. A helpful taxonomy
of MCDM techniques was created by Belton and Stewart in 2022. Additionally, Jim
Suhr's "Choosing by Advantages" (CBA) was introduced as a new category by Arroyo
(2014). Four categories can be used to group MCDM techniques:
1. Goal-programming and multi-objective optimization methods (linear optimization)
2. Value-based methods (e.g., Analytic Hierarchy Process (AHP) and Weighting Rating
and Calculating (WRC))
3. Outranking methods (e.g., ELECTRE)
4. Choosing by advantages (e.g., CBA Tabular Method)
The literature on MCDM methods contains the first three techniques. The fourth
approach is primarily seen in the lean community literature and is not covered in the

publications on MCDM methods or decision-making linked to operations research.
In the Architecture, Engineering, and Construction (AEC) industry, value-based
methodologies are clearly preferred, particularly the AHP method, which is frequently
employed and well-documented in the literature as an alternative. In comparison to
AHP, goal-programming and outranking approaches are less common in the literature.
Most CBA applications are found in the lean community. CBA differs from other
approaches since it focuses on contrasting alternatives by emphasizing advantages.
2.1.5. Choosing by Advantages (CBA) methods:
Definition
Choosing by Advantages (CBA) is a collaborative and transparent decisionmaking system developed by Jim Suhr, which comprises multiple methods. CBA
includes methods for virtually all types of decisions, from very simple to very complex
[7].
“Choosing By Advantages (CBA) is not an individual tool or technique. CBA is a
decision-making system. It is also a decision-making process, not just a step in the
process. The CBA system includes definitions, models, and principles, in addition to


7

tools, techniques, and methods of decision-making. The principles are central. The
definitions and models help explain the principles, and the methods apply the principles.
The CBA system includes methods for virtually all types of money and non-money
decisions, from the simplest to the most complex. Sound decision making is the
foundation of the CBA system.” Jim Suhr.
The principal goal of CBA method is to assist decision-makers in differentiating options
and comprehending the significance of those differences. Decisions in CBA are based
on the positive differences between options' advantages rather than their advantages and
negatives, preventing duplicate counting.
In general, if Factor 1 has a difference between alternatives calling Difference 1 Factor
n has a difference between alternatives calling Difference n, the advantages here can be

understood that between Difference 1 (at factor 1) and Difference n (at factor n), which
is more important.
Sound decision-making, which includes the techniques we currently see applied in our
business, is at the heart of the system.
Sound decision making has four cornerstone principles [8]:
-

The Pivotal Principle – decision-makers must learn and skillfully use sound methods.

-

The Fundamental Rule of Sound Decision-Making – decisions must be based on the
importance of advantages.

-

The Anchoring Principle – decisions must be anchored to relevant facts.

-

The Methods Principle – different types of decisions call for different sound methods
of decision-making.

When CBA is properly implemented, it results in collaboratively made, sound decisions
that have concise and transparent documentation. This is helpful when sharing the
decision with others, the rationale can be clearly understood. It is also helpful when there
is new information or a new stakeholder, and the decision needs to be revisited or
updated.
The wording used throughout CBA is standard. Although the language isn't inherently
"unfamiliar" to most people, it is utilized succinctly and consistently.



8

CBA definitions adapted from Suhr (1999) [7]:
Alternatives: Two or more construction methods, materials, building designs, or
construction systems, from which one or a combination of them must be chosen.
Factor: An element, part, or component of a decision. For assessing sustainability,
factors should represent economic-, social-, and environmental aspects. It is important
to note that CBA considers money (e.g., cost or price) after attributes of alternatives
have been evaluated based on factors and criteria.
Criterion: A decision rule, or a guideline. A ‘must’ criterion represents conditions each
alternative must satisfy. A ‘want’ criterion represents preferences of one or multiple
decision makers.
Attribute: A characteristic, quality, or consequence of one alternative.
Advantage: A benefit, gain, improvement, or betterment. Specifically, an advantage is
a beneficial difference between the attributes of two alternatives
Phases of CBA Decision-Making:
Mossman (2013) [9] determined five phases of decision-making in CBA: stagesetting, innovation, decision-making, reconsideration and implementation

which

decreases waste while increasing efficiency, respect for team members, and project
outcomes including profits.
1. Stage-setting
Describe the problem under discussion and the desirable result. List all the variables and
information that are known and will be used to make decisions. Make a list of everyone
who will be affected by the decision and make sure they are invited to the table for
discussion. There should be at least one representative from each impacted party present.
2. Innovation

Create a number of potential decisions (alternatives) to take into consideration. Make
sure your list is as comprehensive as possible; this is the list that the team will be
working from when conducting the decision-making process. Make sure all participants
in the decision-making process are providing you with a complete picture of the
potential possibilities. Identify each alternative's characteristics. What unique feature


9

does each option offer above the others? At this moment, do not stress about the
significance of these qualities, but rather consider what distinguishes each potential
course of action.
3. Decision-making
This is the part of the decision that many people associate with “CBA” in AEC industry,
the stage where a team goes through the stages below and chooses an alternative.
-

Summarize the attributes of each alternative.

-

List the advantages of each alternative.

-

Decide the importance of each advantage.

-

Choose the alternative with the greatest total importance of advantages.


4. Reconsideration
Review the basis on which the choice was made. Given all the available information,
does the choice seem to be the best one to make? Allow any member who has doubts to
voice them before the choice is made. Everyone who will be impacted by the choice
should be given the chance to express their thoughts on it and how it might affect the
result. Keep in mind that the outcome is what matters most while making a decision.
5. Implementation
Implement the choice made with the result in mind. To find out how the choice functions
in practice and how it may be improved going forward, use the PDCA (Plan, Do, Check,
Adjust) method of continuous improvement.
The Tabular Method, which is used to select between two or more mutually exclusive
alternatives that are not equally expensive, is maybe the most popular CBA technique.
In everyday business, judgments where a team must pick between options that are
mutually exclusive include selecting a building material, a general contractor,
equipment, a building design, and who to recruit. The CBA Tabular Method enables the
transparent documentation of choices that range in complexity from moderate to highly
complicated.


10

According to Arroyo (2015), a procedure of applying CBA method includes 7 main
steps as shown in figure below [10].

Step 1
Identify
Alternatives

Step 2

Define
factors

Step 3
Define
criteria for
each factor

Step 4
Describe
attributes of
each
alternatives

Step 5
Decide the
advantages
of each
alternative

Step 6
Decide the
importance
of each
advantage

Step 7
Evaluate
cost


Figure 2.1: CBA Steps
The advantages of CBA method:
Choosing By Advantages is a method that stands out from others when it comes
to the picking dilemma, which is described as choosing one and only one alternative (or
a combination of alternatives), the best of all.
Consistency, transparency, anchorage to choose context, avoidance of double counting
of data, consensus building, documentation, and ease of decision explanation are all
characteristics of "excellent" decision-making methods.
According to Arroyo (2015), CBA is superior to other MCDM methods in many regards
[11]:
-

CBA is superior to Goal Programming methods when it comes to understanding
what are the relevant factors that differentiate the alternatives. Goal programming
techniques are designed to optimize an unlimited number of options, but when there
are just a few (2 to 10) it makes more sense to use CBA and identify how the
alternatives differ from one another rather than establishing an objective formula.

-

CBA is superior to Value-based methods, when it comes to consistency and
collaboration. Research has shown that the most popular MCDM techniques, AHP
and WRC, are ineffective at removing non-differentiating factors. AHP uses
pairwise comparisons while WRC uses direct comparisons to weigh factors. Since
factors are a representation of a broad idea rather than a context-based evaluation,
they cannot be consistently weighted. Could you state, for instance, that productivity
is more important than safety when picking a construction method? or that
productivity comes second to safety? These are the kinds of inquiries that spark
interminable, pointless debates that reveal nothing about the genuine options that are



11

available for selection. CBA, in contrast, is based on comprehending the benefits of
one alternative. Therefore, CBA helps decision makers to focus on the decision
context and avoid unnecessary discussions.
-

CBA is more practical than outranking methods, because one can create a ranking of
the best alternatives, which is very useful to compare value vs. cost, to prioritize
alternatives, and to allocate money to projects. Outranking methods avoid weighting
factors as AHP and WRC do, but they do not produce a ranking of the alternatives.

-

Finally, CBA, one of the four MCDM techniques now in use, excels in encouraging
cooperation and offering decision-makers a clear justification when making a choice
between limited options (2-10).

2.1.6. Artificial Intelligence and Machine Learning
AI, which stands for Artificial Intelligence, is a branch of computer science. AI
refers to the implementation of intelligent behavior by analyzing conditions with some
degree of autonomy in order to achieve specific goals, solve problems [12]. To put it
simply, AI is machine intelligence generated by human intelligence. This intelligence
can think for itself, actively learn and collect information, simulate the human reasoning
process to make the most optimal decisions based on what has been trained. Artificial
intelligence can handle a larger volume of data, more systematically, and faster than
humans.
A branch of AI is Machine Learning, which is a field of research that provides computers
with the ability to learn on their own based on input data to predict or Make their own

decisions without being specifically programmed [13].
There are many ways to classify Machine Learning, but according to the training
method, Machine Learning is divided into two main groups:
-

Supervised learning: is a method for computers to learn on labeled data, with each
output of new data based on previously known data. Supervised learning is further
subdivided into two main groups: (1) Classification if the assigned labels of the input
data are divided into finite groups. (2) Regression if the assigned label does not
divide into groups but a specific real value.

-

Unsupervised learning: In this case we only have input data without knowing the
output variables or labels, the computer will have to learn on unlabeled data, the