VIET NAM NATIONAL UNIVERSITY HO CHI MINH CITY
HO CHI MINH CITY UNIVERSITY OF TECHNOLOGY
TU ANH NGUYEN
DEVELOP A PROJECT PLANNING METHOD BASED ON BUILDING INFORMATION MODEL (BIM) TO OPTIMALLY
REDUCE ACTIVITY OVERLAPS AND TIME COST
Major: Construction Management Major code: 8580302
MASTER’S THESIS
Trang 2THIS THESIS IS COMPLETED AT
HO CHI MINH CITY UNIVERSITY OF TECHNOLOGY – VNU-HCM
Supervisor: Assoc Prof Long Duc LUONG
Examiner 1: Assoc Prof Hoc Duc TRAN
Examiner 2: Dr Cuong Viet CHU
This master’s thesis is defended at HCM City University of Technology, VNU-HCM on July 13th, 2023
Master’s Thesis Committee:
(Please write down full name and academic rank of each member of the Master’s Thesis Committee)
1 Dr Thu Anh NGUYEN - Chairman
2 Dr Minh Nhat HUYNH - Member, Secretary 3 Assoc Prof Hoc Duc TRAN - Reviewer 1
4 Dr Cuong Viet CHU - Reviewer 2
5 Dr Chau Ngoc DANG - 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: Tu Anh NGUYEN Student code: 2171070
Date of birth: October 16th, 1999 Place of birth: Quang Nam, Vietnam Major: Construction Management Major code: 858032
I THESIS TOPIC:
Develop a project planning method based on building information model (BIM)
to optimally reduce activity overlaps and time costs
Phát triển phương pháp hoạch định dự án dựa trên mơ hình thơng tin BIM để giảm tối ưu sự chồng lấn của các công tác và thời gian – chi phí
II TASKS AND CONTENTS: Optimization in Construction Project III TASKS STARTING DATE: February 2nd, 2023
IV TASKS ENDING DATE: June 10th, 2023
V INSTRUCTOR: Assoc Prof Long Duc LUONG
Ho Chi Minh City, June 10th, 2023
ADVISOR HEAD OF DEPARTMENT
Assoc Prof Long Duc LUONG Dr Long Hoai LE
DEAN OF FACULTY OF CIVIL ENGINEERING
Trang 4ACKNOWLEDGEMENT
I would like to express my deepest appreciation and gratitude to all those who have supported and contributed to the completion of my master thesis in Construction Management as part of the International Master Programs (IMP) at the Ho Chi Minh City University of Technology, Department of Civil Engineering
First and foremost, I would like to extend my heartfelt thanks to my advisor, Assoc Prof Luong Duc Long Your guidance, expertise, and unwavering support throughout the research process have been invaluable Your profound knowledge and insightful suggestions have helped shape and improve the quality of this thesis I am truly grateful for your patience, encouragement, and dedication
I would also like to express my sincere gratitude to the faculty members of the Department of Civil Engineering for their continuous encouragement, wisdom, and valuable inputs Their commitment to academic excellence and their willingness to share their expertise have greatly enhanced my understanding and knowledge in the field of Construction Management
I would like to extend my appreciation to my fellow classmates and friends who have provided me with valuable insights, discussions, and support throughout my academic journey Your presence and camaraderie have made this experience enjoyable and memorable
Additionally, I am deeply grateful to the staff and librarians at the Ho Chi Minh City University of Technology for their assistance and resources, which have greatly facilitated my research
Finally, I would like to acknowledge my family for their unconditional love, encouragement, and understanding throughout my studies Their unwavering support has been the foundation of my success
Thank you all once again for your guidance, encouragement, and support
Trang 5ABSTRACT
With the rise in usage of building information modeling (BIM) systems, there is a greater demand for a construction schedule management system that can make more sophisticated decisions When there is a significant overlap between construction activities, it can lead to poorer performance in those areas As a result, a suitable construction timetable should be created to reduce the overlap of nearby construction operations One potential solution for this problem is an active system This research aims to provide a methodical approach and computer system for simulating an ideal construction timetable that eliminates overlapped tasks and improves operational performance The primary objectives of this study are to identify overlapping activities, apply fuzzy theory, and evaluate the risks associated with schedule overlap problems The genetic algorithm (GA) theory is also applied to optimize the overlap of high-risk activities The study created a four-dimensional (4-D) environment system that utilizes building information modeling (BIM), and it includes a scheduling simulator, as well as fuzzy and GA analysis tools To demonstrate the effectiveness of this approach, the study presented a case study based on a real project
Keywords: BIM implementation in construction projects, 4-D modeling, ratio
Trang 6TÓM TẮT LUẬN VĂN THẠC SĨ
Với sự gia tăng trong việc sử dụng các hệ thống mô hình hóa thơng tin xây dựng (BIM), nhu cầu về một hệ thống quản lý tiến độ xây dựng có thể đưa ra các quyết định tinh vi hơn ngày càng tăng Khi có sự chồng chéo đáng kể giữa các hoạt động xây dựng, nó có thể dẫn đến hiệu suất kém hơn trong các lĩnh vực đó Do đó, cần tạo ra một thời gian biểu xây dựng phù hợp để giảm sự chồng chéo của các hoạt động xây dựng lân cận Một giải pháp tiềm năng cho vấn đề này là một hệ thống đang hoạt động Nghiên cứu này nhằm mục đích cung cấp một cách tiếp cận có phương pháp và hệ thống máy tính để mơ phỏng một thời gian biểu xây dựng lý tưởng giúp loại bỏ các nhiệm vụ chồng chéo và cải thiện hiệu suất vận hành Mục tiêu chính của nghiên cứu này là xác định các hoạt động chồng chéo, áp dụng lý thuyết mờ và đánh giá rủi ro liên quan đến các vấn đề chồng chéo lịch trình Lý thuyết thuật tốn di truyền (GA) cũng được áp dụng để tối ưu hóa sự chồng chéo của các hoạt động rủi ro cao Nghiên cứu đã tạo ra một hệ thống môi trường bốn chiều (4-D) sử dụng mơ hình hóa thơng tin tịa nhà (BIM) và nó bao gồm một trình mơ phỏng lập lịch trình, cũng như các cơng cụ phân tích GA và mờ Để chứng minh tính hiệu quả của phương pháp này, nghiên cứu đã trình bày một nghiên cứu điển hình dựa trên một dự án thực tế
Từ khóa: Triển khai BIM trong các dự án xây dựng, mô hình 4-D, phân tích tỷ lệ
Trang 7AUTHOR’S COMMITMENT
The undersigned below:
Student full name: Tu Anh NGUYEN
Student ID: 2171070
Place and date of born: Quang Nam Province, Vietnam, October 16th, 1999 Address: Binh Tan District, Ho Chi Minh City
With this declaration, the author finishes his master’s thesis entitled “DEVELOP
A PROJECT PLANNING METHOD BASED ON BUILDING INFORMATION MODEL (BIM) TO OPTIMALLY REDUCE ACTIVITY OVERLAPS AND TIME COST” under the advisor's supervision All works, ideas, and materials that was gain from
other references have been cited correctly
Ho Chi Minh City, June 10th, 2023
Trang 8TABLE OF CONTENTS
TABLE OF CONTENTS v
TABLE OF FIGURES viii
TABLE OF TABLES x
LIST OF ABBREVIATIONS xi
CHAPTER 1: INTRODUCTION 1
1.1 General Introduction 1
1.2 Problem Statement 1
1.3 Object and Range of Study 2
1.4 Scope of the Study 3
1.5 Research Methodology 3
1.6 Structure of the Study 4
CHAPTER 2: LITERATURE REVIEW 5
2.1 Definitions and Concepts 5
2.1.1 Schedule 5
2.1.2 Overlapping principle 7
2.1.3 Overlapping time impact 11
2.1.4 Overlapping costs and benefits 12
2.1.5 Overlapping Time-cost tradeoff 12
2.1.6 Fuzzy logic 13
2.1.7 Mamdani and Sugeno Fuzzy Inference Systems 14
2.1.8 BIM implementation in construction planning and scheduling 15
2.1.9 Optimization in Construction 16
2.1.10 Optimization Method 18
2.2 Genetic Algorithms 19
Trang 92.2.2 Advantages of Genetic Algorithm 27
2.3 Previous research 27
CHAPTER 3: RESEARCH METHODOLOGY 29
3.1 Algorithm for Finding Overlapping Schedule in Project Activities 30
3.2 Fuzzy-Based Risk Analysis Algorithm 35
3.3 Algorithm for Optimizing Schedule Overlapping using Genetic Algorithm 40
3.3.1 Schedule optimization application process 40
3.3.2 Function and constraints utilized in the optimization of the project’s schedule 42
3.3.3 The process of generating an initial solution for the GA algorithms 43
3.3.4 Establishing the fitness function 44
3.3.5 Analysis of Genetic Algorithm operation 44
3.3.6 Tradeoff between the total cost of risk and the total overlapping duration 45
3.3.7 Pareto Front 46
3.4 System for Optimizing Schedule Overlapping using BIM-Based Simulation 46
CHAPTER 4: MODEL IMPLEMENTATION AND VALIDATION 48
4.1 Model Implementation 48
4.1.1 Case study 1 48
4.1.2 Case study 2 – Bloomsdale Residence 62
4.2 Model validation 73
4.3 Result Discussion 73
CHAPTER 5: CONCLUSION AND RECOMMENDATION 75
5.1 Conclusion 75
5.2 Research contribution 75
5.3 Future works 76
REFERENCES 77
Trang 101.ANNEX A: Case study 2 detail work in MS Project 81
2.ANNEX B: Case study 2 detail project time 82
3.ANNEX C: Case study 2 detail risk analysis 85
Trang 11TABLE OF FIGURES
Figure 2.1: Activity on Node (AON) (P B Tarigan, 2021)[8] 6
Figure 2.2: Activity on Arrow (AOA) (P B Tarigan, 2021)[8] 7
Figure 2.3: Four types of activity relationships (Adopted from (Prasad, 1996)[11]; (Dehghan, Hazini, & Ruwanpura, 2011)[3] 9
Figure 2.4: The mechanism of activity overlapping (Dehghan & Ruwanpura, 2011)[10] 10
Figure 2.5: Semi-independent activities' overlapping (Dehghan & Ruwanpura, 2011)[10] 10
Figure 2.6: Schedule compression comparison 11
Figure 2.7: Overlapping time impact on the project schedule (Dehghan & Ruwanpura, 2011)[10] 12
Figure 2.8: Overlapping cost function (Dehghan, Hazini, & Ruwanpura, 2011)[3] 12
Figure 2.9: Fuzzy inference example 15
Figure 2.10: Direct Cost, Indirect Cost, and Total Cost in Construction (Hegazy, 2002)[19] 17
Figure 2.11: Optimization in Exact Method (P B Tarigan, 2021)[8] 18
Figure 2.12: General structure diagram of Genetic Algorithm 21
Figure 2.13: The Single Point Crossover Method 24
Figure 2.14: The Two-Point Crossover Method 24
Figure 2.15: Uniform Crossover Approach 25
Figure 3.16: Overall Process of the Model 29
Figure 3.17: Typical condition of schedule overlapping 32
Trang 12Figure 3.19: Membership function for Probability (P), Intensity (I), Output and Fuzzy
Simulink 36
Figure 3.20: Fuzzy rule surface in MATLAB 38
Figure 3.21: A process of schedule optimization using GA to optimize overlapping schedule and time cost 41
Figure 3.22: TF-based solution generation method utilizing activity relationships 44
Figure 4.23: Case study 1 bar chart 49
Figure 4.24: Genetic algorithm with penalty value and average distance in case study 155Figure 4.25: Optimized project schedule 57
Figure 4.26: Pareto front time-cost tradeoff for Case study 1 59
Figure 4.27: Updated schedule for objective 1 optimization 60
Figure 4.28: Updated schedule for objective 2 optimization 61
Figure 4.29: Pareto front of multi-objective optimization 62
Figure 4.30: Some perspective views of the project 63
Figure 4.31: Project’s ground floor plan 63
Figure 4.32: Project's first floor plan 64
Figure 4.33: Risk degree of activity number 20 and 24, respectively 67
Figure 4.34: Multi objective optimization in Case study 2 68
Trang 13TABLE OF TABLES
Table 2.1: Mamdani and Sugeno advantages 14
Table 3.3: Determine ES and EF 30
Table 3.4: Determine LS and LF 31
Table 3.5: Membership function and five Euclidean distance values 35
Table 3.6: If-then rules in MATLAB 36
Table 4.7: The project activities, description, duration and predecessors of case study 148Table 4.8: Case study 1-Calculate project schedule 49
Table 4.9: Overlapping check for activity '2' 50
Table 4.10: Initial SOR value for all activities 51
Table 4.11: Probability (P) and Intensity (I) calculation 52
Table 4.12: Initial overlapping duration of all activities 53
Table 4.13: Case study 1 updated schedule after optimizing 56
Table 4.14: Optimized SOR values for case study 1 57
Table 4.15: New table activity for best solution 1 59
Table 4.16: New table activity for best solution 2 60
Table 4.17: Project's activity, duration, predecessors of case study 2 64
Table 4.18: Top 10 risky activities of the project based on 5 Euclidean distance values66Table 4.19: Movable duration corresponding with objective 1 68
Table 4.20: Movable duration corresponding with objective 2 69
Table 4.21: Movable duration exported from MATLAB 70
Trang 14LIST OF ABBREVIATIONS
BIM Building information modeling
AEC The architecture, engineering, and construction industry
GA Genetic algorithm
CPM Critical path method
PERT Program evaluation and review technique PSO Particle swarm optimization
DEA Data envelopment analysis
DE Differential evolution
SOR Schedule overlapping ratio
RM The risk cost
ES Early start
EF Early finish
LS Late start
LF Late finish
Trang 15CHAPTER 1: INTRODUCTION
1.1 General Introduction
The technology of successfully building information modeling (BIM) in the architectural, engineering, and construction (AEC) industry is one of the most promising By utilizing BIM (Building Information Modeling) technology, a realistic digital representation of a building is generated This model, known as a building information model, encompasses the entire lifecycle of the facility, including planning, design, construction, and operation Architects, engineers, and builders can leverage this model to gain insights into possible design, construction, or operational issues It enables them to visualize and assess the building virtually, facilitating the identification and resolution of concerns before the actual construction process begins The role integration of all project stakeholders is supported by the new AEC revolution known as BIM Current BIM solutions mainly concentrate on presenting design and construction-phase data in a clear and simple manner The basic simulation of construction schedule data is integrated with standard Navisworks functions While these BIM functions enable visualization of construction schedule data, they lack additional decision-making capabilities Such visualization-based BIM functions are classified as passive BIM systems that do not have active decision-making features to offer dynamic solutions
1.2 Problem Statement
Trang 16minimizing concurrent progress between tasks if there are few overlaps in the nearby work area Hossain and colleagues devised a simulation model to approximate the overall duration of a project that incorporates overlapping tasks, as well as to predict the anticipated number of reworks If a project schedule can be optimized successfully, a suitable system to consider would be an active Building Information Modeling (BIM) system equipped with the required decision-making capabilities Such a system would enable efficient and effective decision-making throughout the project lifecycle The results indicate that the anticipated reduction in project duration and the number of reworks are influenced by the reliability of information obtained during earlier stages and the sensitivity of subsequent operations Furthermore, unplanned overlap could result in excessive design and construction work that could be very expensive rather than necessarily shortening the project’s duration The suggested optimization method helps in selecting an overlay strategy by decreasing the expected amounts of reworks while keeping the project completion date [2] If activities overlap, there may also be a shortage of working area and defection
Various schedule compression techniques have emerged in response to the need for project completion in a shorter amount of time Fast-tracking is recognized by the Project Management Body of Knowledge (PMOK) as a method for compressing the timetable Fast-tracking involves doing stages or operations that would typically be done in succession in parallel, or more precisely, by overlapping them As a result of these overlaps, there may be more work and risk [3] Implementing this approach may involve completing tasks without having all the required information, leading to a trade-off between the cost and benefits of time savings However, this also increase the risk of meeting the shortened project timeline [4]
1.3 Object and Range of Study
Object: The purpose of this research is to assess different levels of risk associated with carrying out construction tasks that overlap and to create a systematic approach and computer program for simulating schedules that minimize such overlaps in a specific project Additionally, the model will produce a range of ideal solutions, including the schedule, expected duration, and risk cost for each task.
Trang 171.4 Scope of the Study
To refine the research problem, the study delimits its scope by outlining the following specifics:
a Initially, a method is created for an automated exploration of potential overlaps in the construction schedule
b If required, fuzzy algorithms and risk simulation model are established
c An optimization algorithm that employs the genetic algorithm (GA) is used for activities with higher risks of overlap
d A Building Information Modeling (BIM) tool is utilized to generate a visually dynamic representation of optimized schedule options
e The models are executed using the Genetic Algorithm in MATLAB
f Case studies used in the research are borrowed from prior studies and actual project
1.5 Research Methodology
The following technique was used in this study to achieve the study's objectives:
a Literature study
A literature review of current research developments is carried out to explore and assess the pertinent issue connected to time-cost tradeoff and schedule optimization in terms of the approaches and tools, then searched for prospective improvement that can be achieved The literature review serves as a general overview of overlapping principles and overlapping time-cost tradeoffs
b Model development
A MATLAB programming-based model is developed through a thorough examination of existing research, aiming to strike a balance between time and cost considerations The main objective of this model is to produce a range of optimal solutions that minimize both project duration and expenses
c Validation of the developed model
Trang 18case studies are underway One is a basic model to get at a decent outcome, while the other is an example of how the model can handle a more challenging project
1.6 Structure of the Study
The thesis includes five chapters: - Chapter 1: Introduction
- Chapter 2: Literature review - Chapter 3: Methodology
Trang 19CHAPTER 2: LITERATURE REVIEW
2.1 Definitions and Concepts
Three factors play a significant role in planning and controlling construction projects: time, cost, and quality [5] There are often various pathways in project schedules, and one or more of them is critical In many cases, overlapping non-critical activities are negative to the project On the other side, while overlapping critical activities might be advantageous, it can also turn other noncritical activities become critical ones The overlapping principle, fuzzy logic, the Genetic Algorithm, and the trade-off between time and cost in construction projects will be addressed in more detail in the following part
2.1.1 Schedule
In project management, a schedule is a tool that is most commonly used to plan the project step by step A simple explanation of a schedule is that it's a plan indicating the timing of every task within a project By systematically analyzing each activity and its relationship to the other activity, the project manager will be able to build a project on paper before starting it The schedule determines the start, duration, and finish date of activities in the project Knowing precisely the duration of the activities will have an impact to the project cost For instance, rent an equipment to do an activity without knowing clearly how long the activity will take quickly reduce the planned profit So, the project manager must schedule the whole activities properly and effectively to meet the deadline and not reduce profit
Below is the process for scheduling the project: 1) Identify project activities
2) Determine activities sequence 3) Determine activities duration 4) Perform schedule calculation 5) Revise and adjust
6) Monitor and control
Trang 202.1.1.1 Critical Path Method (CPM)
The critical path method is the sequence of activities that results in the longest total duration It is the shortest possible time for the project’s completion [6]
When any activity in the critical path is delayed, it will impact the overall duration of the project There are two ways to do CPM; they are AOA (Activity on Arrow) and AON (Activity on Node) In AON, an activity is denoted by a box or node, whereas in AOA, the activity is denoted by an arrow AON, on the other hand, is preferred over the AOA network due to its simplicity [7] The two figures below describe the scheduling for AON and AOA, respectively
000000START0022201ESTFEFLFDLSIDLegend:2134133213413440662452044222
Trang 21Start0022A1234443466ESLSIDDurationEFLFLegend:A22A31A41A52
Figure 2.2: Activity on Arrow (AOA) (P B Tarigan, 2021)[8]
2.1.1.2 Program Evaluation and Review Technique (PERT)
PERT (Program Evaluation and Review Technique) is a statistical approach that is utilized to analyze the tasks involved in completing a project It is typically employed for complex and uncertain projects when the specifics and durations of all activities are not precisely defined To use PERT, three-time estimates are assigned for each activity: the optimistic time estimate (To), the most likely or normal time estimate (Tm), and the pessimistic time estimate (Tp) [9] The expected time can then be calculated as follows:
𝑇𝑒 =𝑇𝑜+4×𝑇𝑚+𝑇𝑝
6 (1)
Standard deviation is calculated to see the probability to finish the project within the expected time Standard deviation and variance can be calculated as follows:
𝑆 = 𝑇𝑜−𝑇𝑝6(2) 𝑆2 = (𝑇𝑜−𝑇𝑝6 )2 (3) 2.1.2 Overlapping principle
Trang 22a Dependent activities: The first type of relationship involves an activity that is dependent on another activity for final information
b Semi-independent activities: The second type of relationship occurs when an activity requires only partial information from other activities to begin
c Independent activities: The third type of relationship occurs when there is no information dependency between two activities
d Interdependent activities: The fourth type of relationship involves a two-way information exchange between activities until they are both completed
Trang 23Figure 2.3: Four types of activity relationships (Adopted from (Prasad, 1996)[11]; (Dehghan,
Hazini, & Ruwanpura, 2011)[3]
Figure 2.3 illustrates the process of overlapping two dependent activities, where the start of one activity is reliant on the completion of another activity This is because the information generated by the predecessor activity is necessary for the successor activity However, in order to compress the timeline, the successor activity may intentionally begin before the conclusion of its predecessor activity This can be achieved if the predecessor activity provides the successor activity with some preliminary information before it is completed The successor activity can then begin more quickly by using this initial data and making relevant assumptions and predictions During the time that the two activities are in progress, some intermediate information may also be exchanged Once the predecessor activity is completed, it will provide its final information to the successor activity
Trang 24Figure 2.4: The mechanism of activity overlapping (Dehghan & Ruwanpura, 2011)[10]
Figure 2.5: Semi-independent activities' overlapping (Dehghan & Ruwanpura, 2011)[10]
The concept of overlapping dependent activities, as explained earlier, can be expanded to semi-independent activities by treating the predecessor as two sequential activities (A1 and A2) In this scenario, A1 takes place before the information exchange, followed by A2 (as depicted in Figure 2.5) Consequently, A1 serves as the predecessor for both A2 and B This allows for the overlapping of activity A1 with activity B, utilizing the mechanism of dependent activities, while activity A2 and activity B can overlap seamlessly as they are independent
Trang 25Construction overlap occurs when different stages of construction occur concurrently instead of being completed sequentially, leading to a faster overall construction schedule Fast-tracking is a common cause of construction overlap, as it involves compressing the construction schedule by overlapping tasks that would typically be done separately
While fast-tracking can speed up the construction process, it also increases the risk of construction overlap When different construction stages occur simultaneously, there is a higher likelihood of conflicts arising between workers, materials, and equipment This can lead to delays, rework, and even safety hazards
Fast-tracking also increases the complexity of project management, as it requires close coordination and communication between different teams and contractors This can be challenging, as different teams may have different priorities and schedules, and it can be difficult to ensure that everyone is on the same page
Figure 2.6: Schedule compression comparison
2.1.3 Overlapping time impact
Trang 26subtracting the rework time from the overlapping time However, the study conducted for this research revealed that schedulers do not typically consider the rework period in their scheduling, leading to unrealistic timetables However, in certain industries, risk analysts take the possibility of rework into account when performing schedule risk analyses
Figure 2.7: Overlapping time impact on the project schedule (Dehghan & Ruwanpura, 2011)[10]
2.1.4 Overlapping costs and benefits
Figure 2.8 demonstrates the impact of varying levels of overlap on costs by utilizing bar charts to depict four different scenarios of activity B in relation to activity A The first scenario shows no overlap between activity B and activity A However, in the second, third, and fourth scenarios, the degree of overlap progressively increases to 25%, 50%, and 75% respectively The curve below the bar charts represents the cost of overlapping activities at different degrees of overlap, with the height of the curve proportional to the amount of time required
Figure 2.8: Overlapping cost function (Dehghan, Hazini, & Ruwanpura, 2011)[3]
2.1.5 Overlapping Time-cost tradeoff
Trang 27𝑍 = ∑ 𝐶𝑖𝑗𝑘 − 𝐵𝑒𝑓(𝑇𝑛− 𝑇𝑜𝑙) (4) Where:
𝑖: Index denoting predecessor activities 𝑗: Index denoting successor activities
𝑘: Index denoting degrees of overlapping (overlapping intervals) between predecessor activity 𝑖 and successor activity 𝑗
𝐶𝑖𝑗𝑘: Additional costs imposed on the project because of overlapping 𝑘 between 𝑖 and 𝑗
𝐵𝑒𝑓: Project daily early finish benefits
𝑇𝑛: Normal project duration before overlapping 𝑇𝑜𝑙: Project duration after overlapping
Equation (4) can be solved in two ways: To fix a value for 𝑍 ,and find the minimum 𝑇𝑜𝑙 achievable, or to fix desirable project duration (𝑇𝑑), and find the minimum 𝑍 In this paper, only the second option is addressed as the first option can be solved similarly
2.1.6 Fuzzy logic
Construction projects encompass a multitude of intricate and unique tasks that occur within a dynamic environment [12] Various factors such as unfavorable weather conditions, equipment status, resource availability, site conditions, and labor productivity can significantly influence the project's progress During the pre-construction phase, schedule overlaps between activities can directly impact the overall duration of the project There are two approaches that can be used when considering overlapped activities The initial approach is probability-based, leveraging historical data from previously executed similar activities This data is subsequently employed to assess the likelihood of successfully completing a project within a designated time period The second approach is fuzzy-based, which relies on expert knowledge and estimation [13]
Trang 28included in a set due to its flexibility Each element is assigned a membership value between 0 and 1, indicating how much it belongs to the set The fuzzy logic process consists of three main phases, which are as follows:
a Fuzzification b Fuzzy operation c Defuzzification d Centroid method
e Weighted average method f Mean max method
2.1.7 Mamdani and Sugeno Fuzzy Inference Systems
In this thesis, student will use Mamdani systems in MATLAB software to analyze risk for each activity in the project based on the advantages comparison below:
Table 2.1: Mamdani and Sugeno advantages
Fuzzy Inference System Advantages
Mamdani - Easy to understand and use intuitively
- Suitable for incorporating human knowledge and expertise - Rules are easier to interpret and understand
- Widely accepted by various industries and applications
Sugeno - Computationally efficient
- Compatible with linear methodologies, such as PID control - Effective integration with optimization and adaptive techniques - Ensures smooth continuity of the output surface
- Well-adapted for mathematical analysis
Trang 29elements such as membership functions, logical operations, and if-then rules The tipping problem is used as an example to illustrate the fuzzy inference process for a two-input, one-output, three-rule system In this problem, the fuzzy inference system takes service and food quality as inputs and calculates the tip percentage using the specified rules
1 When the service is unsatisfactory or the food is spoiled, the resulting tip tends to be minimal
2 If the service is satisfactory, the tip typically falls within an average range 3 When the service is exceptional or the food is delightful, it is common to provide
a generous tip
Figure 2.9: Fuzzy inference example
The fuzzy inference process has the following steps: 1 Fuzzification of the input variables
2 Application of the fuzzy operator (AND or OR) in the antecedent 3 Implication from the antecedent to the consequent
4 Aggregation of the consequences across the rules 5 Defuzzification
2.1.8 BIM implementation in construction planning and scheduling
Trang 30Information Model Standard provides a definition of BIM as a digital model that captures both the physical and functional attributes of a facility, serving as a reliable reference for informed decision-making throughout the entire life cycle of the building [14] By utilizing BIM, it becomes feasible to visually represent the building's elements and intricate details, including materials, dimensions, geometric arrangements, structural aspects, and design limitations
According to Wang & Chien [15], BIM is predominantly used in the design stage of the project life cycle However, only 45% of respondents were aware of BIM being used in the construction phase, and out of these, 52% reported using a BIM model for construction planning and scheduling Among the respondents who had observed BIM being utilized for construction planning and scheduling, 94% perceived that the technology had increased the effectiveness of project management
2.1.9 Optimization in Construction
Optimization is a mathematical process used to find the highest or lowest value of a given objective within a feasible set In general, optimization involves identifying the best available values from a set based on predetermined criteria This is achieved through the use of an objective function, which is a mathematical representation of the criteria The construction industry relies heavily on optimization due to the complexity and unique nature of construction projects It provides a fast way to do simulations for analyzing and planning the project [16] There are two types of optimizations, which are:
a Single objective optimization
This type of optimization only has a single objective function or set of requirements The goal is to shorten the project's time, reduce the overall cost, or increase the profit The optimal answer to the specified aim is what single objective optimization aims to accomplish The result of this will be the optimum possible solution This tool proves valuable in providing decision-makers with a comprehensive understanding of the challenge at hand However, it often falls short in generating a diverse range of alternative solutions that effectively balance multiple objectives [17]
b Multiple objective optimization
Trang 31objectives of minimizing both time and cost, as well as time-cost-quality optimization, which maximizes quality while minimizing both time and expense When planning and reviewing a building project, a variety of objectives are offered for trade-off, including those related to energy, the environment, risk, and even life cycle performance [18] It should be noticed that the goals are in opposition This means that reducing the time will result in increased costs since more resources will need to be added to the project On the other hand, it should be noted that shortening the project duration does not always result in an increase in direct costs, as it can also lead to a reduction in indirect costs, ultimately lowering the total cost of the project However, if the duration is excessively shortened, it may lead to an increase in costs
Figure 2.10: Direct Cost, Indirect Cost, and Total Cost in Construction (Hegazy, 2002)[19]
There is no single best solution when there are multiple objectives to optimize; instead, there are many diverse solutions that may be developed Pareto optimum solutions are those that give the best trade-off between the objectives [20] There are three options to consider while comparing the various solutions, and they are:
1 X1 dominates X2 2 X1 dominated by X2
3 X1 and X2 are not dominated each other
Trang 322.1.10 Optimization Method
There are three most used optimization methods for time cost tradeoff problems (TCT), they are:
a Exact Method
The mathematical technique is also known as the exact method This approach ensures that the problem will be solved in the best possible way However, the level of work required increases exponentially with the complexity of the challenge This technique typically divides the problem into smaller ones, so solving it Linear programming and dynamic programming are two examples of this approach The mathematical optimization under two restrictions is shown in the figure below
Figure 2.11: Optimization in Exact Method (P B Tarigan, 2021)[8]
b Heuristic Method
In addition to exact methods, heuristic methods use practical methods in a shorter amount of time than exact methods Although it still achieves the purpose, it does not ensure that it will be the best answer Numerous researchers discovered that this algorithm may become stuck in local optima and be unable to find the overall best answer Local optimum refers to the optimal result (for instance, the minimum point it produces), but global optimum denotes the minimum point among all points, with no other point being superior to it This algorithm then resulted from it The algorithm that was created is known as a metaheuristic algorithm
Trang 33The next level up from a heuristic is a metaheuristic Almost every optimization issue can be solved using this technique, and it has been used to do so A better solution can be found by using a metaheuristic algorithm to investigate the problem area Meta-heuristic algorithms have recently been praised for their use in project development [21] Many scientists and engineers from many disciplines have employed metaheuristic algorithms to address optimization issues that came up in their professions Garg (2016) suggested combining the genetic algorithm (GA) with particle swarm optimization, two well-known restricted optimization methods (PSO) To handle limited optimization issues, Garg (2019) recently merged evolutionary algorithms with gravitational search The proposed method GSA-GA was validated using nine well-known structural engineering design issues Rahimian (2019) evaluated the efficiency of data envelopment analysis (DEA) using differential evolution (DE) There are so many kinds of metaheuristic algorithms, some of them are:
Metaheuristics are advanced optimization techniques that can be applied to almost any optimization problem, and have been widely used to find better solutions They are particularly useful in project development, and have been employed by scientists and engineers across various disciplines Garg (2016) proposed a combination of the genetic algorithm (GA) and particle swarm optimization (PSO) to address certain optimization issues, while Garg (2019) combined evolutionary algorithms with gravitational search to handle limited optimization problems Rahimian (2019) also evaluated the effectiveness of data envelopment analysis (DEA) using differential evolution (DE) There are various types of metaheuristic algorithms available, including:
1 Particle Swarm Optimization 2 Ant Colony Optimization 3 Genetic Algorithm
4 Simulated Annealing 5 Differential Evolution 6 Symbiotic Organism Search
2.2 Genetic Algorithms
Trang 34the fittest principle Stronger species in nature can live longer, giving them more opportunities to mate and pass on their powerful genes to future generations Weaker animals have shorter lifespans and are less likely to reproduce; their weak genes will die with them GAs solves complicated multi-objective optimization problems using the same method They produce an initial population of solutions at random These solutions are referred to be genomes or chromosomes Each chromosome is a series of genes, and each gene can have several values Each solution's fitness is determined by comparing its performance A chromosome is composed of multiple genes, forming a string, and each gene can hold various values The fitness of each solution is evaluated by assessing its performance against an objective function
In order to improve the solutions, the initial population of solutions undergoes evolution This involves combining the more robust genes from different solutions through marriage or crossover, resulting in the creation of new solutions with offspring genes These new solutions are evaluated, and if they outperform the weakest solutions in the population, they are incorporated This iterative process continues indefinitely, aiming to generate increasingly superior solutions until an optimal population is obtained The fittest member of the population represents the best solution
Occasionally, a random offspring chromosome is chosen, and its genes are altered to create a distinct chromosome This process, known as mutation, serves to prevent being stuck in local optima and promotes exploration of the solution space [22]
Genetic Algorithm (or any evolutionary program) solving a specific problem must consist of the following five components:
1 A representation of the genetic material for the solutions to the problem 2 A method for initializing the initial population
3 An objective function that serves as the environment to evaluate the fitness of the solutions
4 Genetic operators that manipulate the genetic material
Trang 35START
Create initial, random population of organisms
(potential solutions)
Evaluate fitness for each organism
Optimal or good solution found?
Reproduce and kill organisms
Mutate organismsEND
Figure 2.12: General structure diagram of Genetic Algorithm
2.2.1 Genetic Operators
2.2.1.1 Selection
The process of choosing parents for crossover is referred to as selection The main goal of selection is to identify the individuals in the population with higher fitness levels and allow them to mate, resulting in offspring with improved fitness values The selection process involves randomly selecting chromosomes from the population based on their
fitness function There are two primary selection methods: proportionate selection and
ordinal-based selection
Trang 36Roulette Wheel Selection
The Roulette Wheel Selection method operates by likening the population to a roulette wheel, where each individual's slot size corresponds to their fitness level in a proportional manner To make a random selection, the wheel is spun and a virtual ball is thrown in The likelihood of the ball landing in a particular slot is proportional to the slot's arc, which corresponds to the fitness level of the individual The individual's probability of selection can be calculated using this method
𝑝𝑖 = 𝑓𝑖∑𝑁𝑗=1𝑓𝑖
Where 𝑓𝑖 is the fitness of the 𝑖𝑡ℎ individual and 𝑁 is the number of individuals The following depicts the approach visually:
Rank Selection
Rank Selection is a method that orders the population according to their fitness values, with the fittest individual given a rank of N and the weakest with a rank of 1 The selection of potential parents is followed by a tournament, which determines the chosen individual to be a parent Various methods can be employed to conduct this tournament, including two potential approaches:
1 To select a pair of individuals, a random process is employed A random
number, R, is generated within the range of 0 to 1 If R is less than a specific parameter value, r, the first individual is chosen as a parent Conversely, if R is greater than or equal to r, the second individual is selected as the parent This process is repeated to determine the second parent The value of r is a parameter
that governs the selection method
2 Select two individuals randomly, and the one with the highest fitness score is selected as the parent Repeat this process to select the second parent
Trang 37Random Selection
This technique randomly picks a parent from the population
Stochastic Uniform Selection in MATLAB
The default selection method in MATLAB's genetic algorithm and multi-objective genetic algorithm functions is stochastic uniform selection This method is a modified version of the roulette wheel selection approach, where individuals are chosen based on their fitness with a probability proportional to it However, stochastic uniform selection employs a uniform random sampling technique
2.2.1.2 Crossover
Crossover, also known as mating, is a genetic operation where two individuals are combined in the hope of producing a more fit offspring [23] During crossover, a random location on the chromosomes of the two parents is selected as the crossover site The genes on either side of this site are then copied to the offspring, resulting in a new candidate solution that inherits genetic information from both parents The fitness value of this offspring is then evaluated to assess its level of fitness
Single Point Crossover
Trang 38Parent 11 0 1 1 0 0 1 0
Parent 21 0 1 0 1 1 1 1
Child 11 0 1 1 0 1 1 0
Child 21 0 1 0 1 0 1 0
Figure 2.13: The Single Point Crossover Method
Single Point Crossover
The figure below visually depicts the process of Two-Point Crossover An analogous approach to Single-Point Crossover, but utilizing two loci for exchange of genetic material
Parent 11 1 0 1 1 0 1 0Parent 20 1 1 0 1 1 0 0
Child 11 1 0 0 1 1 1 0Child 20 1 1 1 1 0 0 0
Figure 2.14: The Two-Point Crossover Method
Trang 39The Uniform Crossover Operator is a technique used to create offspring by randomly selecting genes from each parent based on a binary crossover mask This mask determines which genes are chosen from each parent, allowing for a mix of genetic information from both parents in the offspring If the value of the binary mask is 1, the genetic information is copied from the first parent, while a value of 0 indicates that information will be sourced from the second parent The distribution of the binary values is denoted the mixing ratio Figure below depicts the technique
001011Individual 1001011Mask001011Individual 2001011Offspring
Figure 2.15: Uniform Crossover Approach
2.2.1.3 Mutation
Mutation is the key driver of diversity in the candidate solution set or search space [23] Mutation is a technique applied after crossover to promote diversity in the search space and prevent the algorithm from being stuck in local optima By randomly modifying genetic building blocks, mutation introduces new genetic structures to the population and allows the algorithm to explore the entire search space It ensures ergodicity by making it highly unlikely to generate identical solutions from any population state
Reversing
To generate a new offspring artifact, a specific locus within the chosen binary genome representation is selected, and the bit at that location is flipped This process results in the selection of a parent and the creation of a modified offspring
Trang 40The mutation probability, denoted as 𝑃𝑚, is a crucial parameter in the mutation process, as it determines the frequency at which certain parts of the chromosome will be mutated If mutation probability is set to 0%, the artefacts generated by the crossover operation are considered as the ultimate offspring after mating Conversely, if the mutation probability is set to 100%, the entire chromosome will be subjected to the mutation operator [23].
2.2.1.4 Replacement
Following each evolution cycle, the replacement stage plays a pivotal role in substituting older members of the current population with new individuals Two common methods of replacement in genetic algorithms are generational updates and steady-state updates In generational updates, N/2 children are created from a population of N individuals to evolve and establish the subsequent generation population This means that the entire parent selection is replaced in order to create a new population for the next generation This method ensures that an individual can only breed with others from the same generation Conversely, the steady-state update approach involves inserting new individuals into the population as soon as they are generated, as opposed to creating an entirely new generation at each cycle When adding a new individual, one must replace an older member, aiming to replace the least fit member
Random Replacement
Random replacement is a strategy in which two individuals are randomly chosen from the population and replaced by two offspring generated from them The parents of these offspring are also considered as candidates for selection, which can be advantageous for smaller populations as it enables the replacement of weaker individuals and potentially enhances the effectiveness of the search process
Weak Parent Replacement
The Weak Parent Replacement strategy involves replacing the weaker parent of a pair that produces two offspring with the stronger child By doing so, it favors the selection of fitter individuals and contributes to an increase in the overall fitness of the population However, this approach may reduce the diversity of the mating pool