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研究生: 馬辉暖
Maji - Hendra
論文名稱: 應用貝式信念網路量化成本超支風險
Using Bayesian Belief Networks in Quantifying Cost Overrun Risk
指導教授: 楊亦東
I-Tung Yang
口試委員: Jui-Sheng Chou
Jui-Sheng Chou
Jyh-Bin Yang
Jyh-Bin Yang
Po-Han Chen
Po-Han Chen
學位類別: 碩士
Master
系所名稱: 工程學院 - 營建工程系
Department of Civil and Construction Engineering
論文出版年: 2011
畢業學年度: 99
語文別: 英文
論文頁數: 137
外文關鍵詞: Bayesian Belief Networks, Cost Overrun, Road
相關次數: 點閱:251下載:3
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  • In our daily life, we are circled by various risks, and we continually work for better approaches to quantify and to manage risks. Risk Management has become a foremost part of Project Management. An important part of Project Risk Management (PRM) is risk analysis, which efforts to measure risk and its effect on various project indications such as time, cost and quality. Cost is basic to the successful completion of projects. By focusing on the tradeoff between project indications, this thesis concentrates on project cost management under uncertainty.
    This thesis aims to show that BBNs modelling can enhance project risk assessment. A literature review explores the important limitations of the current practice of the project costing under uncertainty. Field's survey to 18 building projects in the Surabaya and online questionnaire survey was also conducted involving experts, company management, project leader, project manager, supervisor, field engineering, sub-contractors, and suppliers. Thirteen significant factors causing cost overrun in construction projects in Indonesia are identified as the result of a comprehensive literature survey and ratings by construction professionals in Indonesia through factor analysis. Furthermore, this thesis analyzes the cause-effect relationships among the identified factors. Following this, 18 cause and effect relationships among the 13 factors are established based on the expert survey. Cost Risk Analysis using Bayesian (CRAB), is proposed, which applies BBNs for performing cost overruns probability calculation and performs the interaction between the variables and predicts cost risks in a manner that attaches probabilities to each degree of cost overrun. Hopefully can be useful for owners in determining more realistic decision-to-build project budget.

    Abstracti Acknowledgmentsii Table of Contentsiii List of Figuresvi List of Tablesvii List of Equationsviii Chapter 1 Introduction 1.1. Research Background 1 1.2. Objectives of Research 6 1.3. Scope of Research 6 1.4. Research Methodology and Process 7 1.5. Organization of Thesis 9 Chapter 2 Literature Study and Reviews 2.1. Introduction 10 2.2. Cost Management 11 2.2.1.Project Cost Estimating 12 2.2.2. Determining Budget 14 2.2.3. Project Cost Control 14 2.3. Risk Management (RM) 16 2.3.1. Risk identification 16 2.3.2. Risk Analysis Techniques 18 2.3.2.1. Qualitative Risk Analysis 18 2.3.2.2. Quantitative Risk Analysis 19 2.3.3. Monitor and Control Risks 21 2.4. Cost Overrun 22 2.4.1. Defective Work 22 2.4.2. Delays 23 2.4.3. Construction Waste 23 2.4.4. Design Changes 24 2.5. The Need of New Approach in Construction Cost Overrun Analysis 26 Chapter 3 Research Methodology 3.1. Conceptual Research Framework 27 3.2. Phase 1: Qualitative Analysis 29 3.2.1. Graphing Qualitative Variables 31 3.2.2. Factor Analysis 32 3.3. Phase 2: Quantitative Analysis 33 3.3.1. Relationship Identification 33 3.3.2. Relationship Quantification 35 3.4. Perceptions of Experience in Projects 36 3.5. The Bayesian approach to probability and statistics 36 3.5.1. Bayes’ Theorem 37 3.5.2. Bayesian Networks 38 3.5.3. BBNs and Project Risk Management 42 Chapter 4 Models and Result 4.1. Respondents Profile 43 4.2. Risk list 48 4.2.1. Risk List in Building and Road Construction in Indonesia 49 4.3. Factors Influencing Construction Cost Overruns 61 4.3.1. Validity and Reliability Test 61 4.3.2. Factor Analysis 63 4.4. Relationship Identification 64 4.5. Model Structure 68 4.6. The probability 73 Chapter 5 Model Validation 5.1. The challenge in project risk analysis methods 77 5.2. Real Project Example 79 5.2.1. Project A 80 5.2.2. Project B 82 5.2.3. Project C 82 5.2.4. Project D 83 5.2.5. Project E 83 5.2.6. Project F 85 Chapter 6 Conclusion 6.1 Summary 89 6.2. Model Limitations 91 6.3. The Path Forward 91 References 92 Appendix A. Questionaire (page 1) 99 B. Correlation (validity test)102 C. Reliability test109 D. Factor analysis110 E. Second questionair result114 F. Third Questionnaire117 G. Probability survey result 122

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