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
Trang 2HO 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,VNU- HCM 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
Trang 3VIETNAM 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 Student ID:
2170311
Date of birth: 21/10/1993 Place of birth: Khanh Hoa
Major: Construction Management Major ID: 8580302
I THESIS TITLE (In Vietnamese): Kết hợp mơ hình thơng 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)
Trang 4ACKNOWLEDGEMENTS
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
Trang 5ABSTRACT
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 design-construction 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
Trang 6Các tòa 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, Mơ hình thơng tin xây dựng (BIM) và phần mềm trong hệ sinh thái này không chỉ được sử dụng để mơ hình hóa (3D), mơ phỏng thời gian (4D), đo lường khối lượng (5D) mà còn dùng để mô 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 q trình mơ phỏng năng lượng
Trang 7THE 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 DESIGN-CONSTRUCTION SOLUTIONS TOWARD SUSTAINABLE DESIGN-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
Trang 8CHAPTER 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
Trang 93.5.3 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
Trang 105.3.2 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
Trang 11TABLE 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
Trang 12DesignBuilder 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
Trang 13FIGURE 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
Trang 14AAC: 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
Trang 15CHAPTER 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:
Trang 16option 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
Trang 17CHAPTER 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 long-term 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)
Trang 18rigorously 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]
Trang 19incorporation 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 energy-efficient 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 decision-making processes Roy (1974) categorized various decision-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)
Trang 20objectives 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 decision-making 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]
Trang 21tools, 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
Trang 22Alternatives: 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: stage-setting, 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
Trang 23does 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
Trang 24steps as shown in figure below [10]
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
Figure 2.1: CBA Steps
Trang 25available 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
Trang 26data modeling aims to provide the computer with knowledge as well as understanding of the data, based on which the algorithm can classify data into categories similar clustering or dimensionality reduction Unsupervised learning [13] [14]
Working progress with Machine Learning is performed in figure below:
Criteria for evaluating efficient energy use
For each green building rating system, a certified building must comply with the main groups of criteria including energy saving, economical use of water, sustainable use of materials, sustainable location and environmental quality assurance inside the building In which, the criterion of energy saving is the most important criterion, including:
Collecting Data Users need to provide data sets by self-collecting or
obtaining previously published orthodox datasets
Preparing Data
Normalize data, remove unnecessary attributes, assign data labels, encode some features, extract features,
reduce data but still ensure results.
Training the Model
Evaluating the Model
Improving the Model
The user trains the model or lets it learn on the data collected and processed in the first two steps
After training the model, use the scales to evaluate the model Depending on the different scales, the model is also evaluated as good or not Model accuracy above
80% is considered good
After evaluating the model, the models with poor accuracy need to be retrained, we will repeat from step
3, until the expected accuracy is reached.
Trang 27- Ensure the building uses minimal energy by meeting the requirements of the mandatory codes and regulations;
- There are design solutions to help take advantage of the natural climate and the site to minimize the need for mechanical cooling and heating of the building, while still ensuring comfort for the occupants;
- Perform simulation of total energy used in the building, thereby identifying and comparing design solutions using energy efficiency;
- Design to optimize the thermal efficiency of the building envelope;
- Measures to reduce energy consumption to cool the space inside the building; - Measures to reduce energy consumption of artificial lighting systems for buildings; - Ensure effective control and management of the building's energy consumption
systems;
Trang 282.2 Relative research
Table 2.1: Summary of some previous relative research
No Author Topic Methodology Advantages Disadvantages
[01] M H Elnabawi (2020) [15] Building Information Modeling-Based Building Energy Modeling: Investigation of Interoperability and Simulation Results
- Testing energy performance using the BIM-based BEM model
Proposed BIM-based BEM to solve design sustainable
construction
Not propose a method to select optimal design options [02] K Bataineh and A Al Rabee (2022) [16] Design Optimization of Energy Efficient Residential Buildings in Mediterranean Region - Determining cost-optimal efficiency packages by dynamic simulation software DesignBuilder and a
building energy optimization software
Consider energy performance and cost of design option
Not consider other factors that impact design selection [03] F J Montiel-Santiago, M J Hermoso-Orzáez and J Terrados-Cepeda (2020) [17]
Sustainability and Energy Efficiency: BIM 6D Study of the BIM Methodology Applied to Hospital Buildings Value of Interior Lighting and Daylight in
- Using Revit and its plugins to simulate energy
consumption
Applied BIM-based BEM to solve design sustainable
construction
Trang 29No Author Topic Methodology Advantages Disadvantages Energy Simulation [04] H H Hosamo, M S Tingstveit, H K Nielsen, P R Svennevig and K Svidt (2022) [18] Multi-objective optimization of building energy consumption and thermal comfort based on integrated BIM framework with machine learning-NSGA II
- Simulating building energy in combination with BIM,
Machine learning (ML),
multi-objective optimization, and visual programming
Applied BIM-based BEM and Machine Learning to solve design sustainable construction
Not propose a method to select optimal design options [05] S Liu, X Meng and C Tam (2015) [19] Building information modeling-based building design optimization for sustainability
- Integrating functions of modeling, simulation, analysis of thermal and lighting performance, and database in BIM in the sustainable building design process to estimate annual energy demand
- Using a PSO-based
optimization process to find potential design solutions - Finding optimum design scheme by pareto-optimal solutions
Applied BIM-based BEM and
Optimization
algorithm to find the optimal design solution
Trang 30No Author Topic Methodology Advantages Disadvantages [06] Arroyo, P., Tommelein, I., and Ballard, G (2012) [20] Deciding a sustainable alternative by 'choosing by advantages' in the ACE industry
- Interviewing experts in the US to gather real application
examples and gain
understanding of decision-making practices in green building design
- Application of two methods (AHP and CBA) to select sustainable alternatives in the AEC industry
- Comparing two methods AHP and CBA
Recommended that CBA should be incorporated in the lean construction body of knowledge
Not mention how to indicate the level of importance of factors affecting the decision
[07] Arroyo, P.,
Tommelein, I D., and
Ballard, G (2015) [21]
Comparing AHP and CBA as Decision Methods to Resolve the Choosing Problem in Detailed Design
- Literature study to identify
the documented MCDM
practices in the AEC
industry
- Studying in - depth applications of AHP and CBA in the AEC industry - Comparing and contrasting AHP and CBA in a case study using hypothetical
Found that CBA was superior to AHP in the context chosen in research
Trang 31No Author Topic Methodology Advantages Disadvantages
preferences with data from a real project
[08] Schöttle, A., &
Arroyo, P (2017) [22]
Comparison of Weighting-Rating Calculating, Best Value, and Choosing by Advantages for Bidder Selection
- Conducting a literature
search comparing WRC,
BVS, and CBA
- Building a case to compare the methods in the context of bidder selection based on the tendering procedure of the real project
Found that CBA provided additional benefits for helping public clients to differentiating between bidders
Not mention how to indicate the level of importance of factors affecting the decision
[09] Ngoc Son
Truong, Ngoc Tri Ngo, Anh Duc Pham (2021) [23]
Forecasting Time-Series Energy Data in Buildings Using an Additive
Artificial Intelligence Model for Improving Energy Efficiency
- Building artificial neural network to predict energy consumption Propose a model to predict energy consumption in buildings by artificial neural network
Not propose a method to select optimal design options as well as consider other factors that impact design selection [10] Ngoc-Tri Ngo, Thi Thu Ha Truong (2022) [24] Proposing a hybrid metaheuristic optimization algorithm and machine learning model for energy use forecast in
non-residential buildings
- SAMFOR model combines the support vector regression
(SVR) and the firefly
algorithm (FA) with the appropriate seasonal auto-regression moving average (SARIMA) model
Propose a combined machine learning model to predict electricity usage data in buildings
Trang 32Identifying objectives of research:
Combining BIM and CBA to select Design-Construction solution toward
sustainable construction
Identifying factors affecting to decision of selecting Design-Construction solution through previous studies, expert s opinion
Collecting data about level of influence of factors through questionnaire surveys
Statistical processing by SPSS, MS Excel
Evaluating the level of importance of factors
Creating 3D models with the characteristics of a typical office by
DesignBuilder software
Identifying primary simulation variables and their ranges of value
Simulation processing by DesignBuilder software
Energy consumption datasets with different values of variables
Creating RF model to predict energy
consumption
Defining design alternatives having the optimal energy consumption
Using CBA to select the best design alternative
Conclusion/Suggestion
Figure 3.1: Research procedure
CHAPTER 3: METHODOLOGY
3.1 Research procedure
Trang 33The research begins by defining the goal of the project, which is Combining BIM and CBA to select Design-Construction solutions towards sustainable construction To use the CBA method in selecting the optimal design alternatives, two important factors to determine are the potential design alternatives to choose from and the importance of the factors influencing the selection decision The study carried out two parallel processes as follows:
The first is the process of determining the factors affecting the decision to choose a design option and ranking the importance of those factors This process begins with the identification of factors by referring to the research conducted and the opinions of industry experts Then, the author conducts a survey to assess the importance of the factors After summarizing the results and statistical analysis using SPSS, the author determined to rank the importance of the factors
Trang 34 Collecting data procedure
Designing survey questionnaires
Questionnaire is one of the most popular research tools used to collect primary data and information from many people Data collection by survey panel makes it easy to get information from a large number of respondents, done in a short time, with the right research objectives and the right subjects to survey
Research results depend a lot on the techniques of designing the Questionnaire, so it is necessary to build the Questionnaire in a scientific and clear way, identify the right research objectives, be academic but also easy to read understood to ensure the objectivity as well as the reliability of the data obtained
The Questionnaire is designed based on documents and synthesis of previous studies,
Referring expert s opinionsDesigning questionnaire following
previous studies
Processing survey to collect data
Filtering data
Analyzing dataFail
Pass
Trang 35combined with consultation with experts knowledgeable in the field of green building design consultancy or buildings with application of energy saving measures energy saving Then, conduct a mass survey to collect data The data collected is mainly from the answers of the surveyed subjects, so the Questionnaire should be designed reasonably to ensure the reliability as well as the objectivity of the research
Survey participants will rate the importance of factors affecting the decision to choose a design option towards sustainable construction on a scale of 0 to 100 Let the survey participants visualize how to score the importance of the factors, the author suggested dividing the scale from 0 to 100 using a five-level Likert scale:
0-20: Not at all important 21-40: Little important 41-60: Moderately important 61-80: Quite important 81-100: Very important
The survey participants are architects, engineers and individuals who have participated in or have knowledge about Green Buildings or energy efficient buildings, are working in the position of Investors, Design Consultants, Project Management Consultants, Cost and Contract Consultants and Contractors The selection of the appropriate survey
participants is mandatory and affects the survey results Determining the sample size
Determining the sample size is an important step to increase the accuracy of the research results According to Bollen, the research sample must be at least 5 times the number of observed variables [25] According to Hoang Trong and Chu Nguyen Mong Ngoc, the selected sample size must be at least 4 or 5 times the number of observed variables [26]
Therefore, with the number of solutions, equivalent to the initial observation variable of 12, the estimated sample size will be about 48-60 samples
Sampling technique
Trang 36methods commonly used: (1) Non-Probabilistic Sampling and (2) Probabilistic Sampling
This study uses Non-probabilistic sampling method with convenience sampling technique Choosing this technique saves time and money, in addition, this technique is also suitable in exploratory research and hypothesis testing
The author made a questionnaire using google form tool, then sent the survey questionnaire to survey subjects through channels such as email, Zalo, Facebook Due to time and cost limitations for implementation, the majority of companies and survey subjects selected were from acquaintances, the author's working partners instead of all other companies and individuals working in the field of construction in Ho Chi Minh City Therefore, although the reliability may not be as high as the probability sampling methods, the convenience sampling technique is acceptable in this thesis
Data Filtering
In order to increase the reliability of the data collection results, the author conducts screening to remove invalid answer sheets, specifically as follows:
- The answers are graded according to a fixed rule, or choose only one answer - The answer choices are missing, or choose more than one answer
Table 3.1: Statistics on the amount of data collected
No Description Quantity Percentage
1 Total of sent questionnaires 60 100.0%
2 Answered questionnaires 53 88.3%
3 Illegal answered questionnaires 1 1.7%
4 Results 52 86.6%
3.3 Data Analysis Tools
3.3.1 Testing the reliability of the scale
Trang 37reliable, and how they are correlated with each other This study tests the reliability of the scale using Cronbach's Alpha coefficient:
The mathematical formula of the Cronbach’s Alpha coefficient [26]:
𝛼 = 𝑁𝜌
1 + 𝜌(𝑛 − 1)
In this formula: ρ is the average correlation coefficient between the observed variables and N is the number of factors
Table 3.2: Meaning of Cronbach’s Alpha coefficient values
Cronbach’s Alpha Reliability Level
α≤0.9 Excellent 0.8≤α<0.9 Good 0.7≤α<0.8 Acceptable 0.6≤α<0.7 Questionable 0.5≤α<0.6 Poor α<0.5 Unacceptable 3.3.2 Rating method
From the data obtained through the survey questionnaire, the author uses the average value of the score to evaluate the importance of the factors in choosing the design - construction in the CBA method To apply to the CBA method, the average score of the factors will be converted back In which the highest score will be converted to 100, the remaining average points will be converted according to the corresponding ratio
3.3.3 One Sample T-Test
Trang 38Hypothesis of One Sample T-Test is established as follows: {𝐻𝑜: µ = 50𝐻1: µ ≠ 50
In this, µ is the mean value of a sample The confidence level used in this One Sample T-Test is 95%
3.3.4 Multi-sample testing
One-way ΑNOVA test was used to determine whether there are any significant differences in mean between subjects of multiple independent groups The test for the problem is performed as follows:
Ho: µ1=µ2= …=µi=…=µn
H1: at least there is a value µi which is different from others
In this 1, 2, …, µi is the mean of the independent variables The confidence level used in this Multi-sample testing is 95%
One-way ANOVA analysis procedure as shown below:
Levene Test
Sig Levene > 0.05
Variances between groups have the similar values
Using results in ANOVA table(F test)
Sig Levene < 0.05
Variances between groups have heterogeneous values
Using results in Robust tests table(Welch test)
Sig F < 0.05The mean values of the
groups are different
Sig F > 0.05The mean values of the groups are not different
Sig Welch < 0.05The mean values of the
groups are different
Sig Welch > 0.05The mean values of the groups are not different
Trang 393.4 Energy simulation software: DesignBuilder
DesignBuilder is a dedicated building energy simulation software based on the open source EnergyPlus compute kernel, a tool that helps architects and engineers control energy, carbon, lighting and environmental impact assessment DesignBuilder is developed with high simulation performance along with an intuitive and simple interface, thus bringing many outstanding advantages such as: The best energy solution will be selected easily by comparing calculation results data; Improve the design in stages to meet the needs of the investor; Manipulate rendering and simulation (including large buildings, complex architecture) quickly with high accuracy; Support importing models created by other popular construction software such as BIM, AutoCAD…; Simulation results can also be presented in the form of graphs, illustrations or report formats suitable for LEED, LOTUS certifications… [27]
DesignBuilder works on a core of 3-D modeling and multiple modules that work together to provide advanced analysis for specific goals: The 3-D modeling core module supports construction fast and easy construction visualization; accompanying tool modules such as solar visualization for building shading, simulation with EnergyPlus for energy analysis and thermal comfort; Radiance algorithm is used to calculate illuminance and natural lighting ratio; the user-friendly interface contributes to the easy and accurate design of the HVAC system; consider the cost of construction investment with the carbon emissions of the building; select the most optimal design option based on the comparison results corresponding to each project's objectives; analyze results and produce reports based on criteria in green building evaluation systems; Simulate the direction of air movement inside and outside the house to calculate readings of pressure, wind speed, temperature and thermal comfort limits… [27]
Trang 403.5.1 General
Random Forest (RF) is a supervised learning algorithm used to solve classification and regression problems, proposed by Breiman in 2001 [28] Studies have shown that RF has a number of outstanding advantages such as: can process data with many attributes, fast learning process, give prediction results with high accuracy, so in recent years it has become very popular [29]
RF is a validated classification algorithm based on decision trees and improved Bagging and Bootstrapping techniques The RF learning process involves using input values randomly, or combining them at each node in the process of building each decision tree [30]