Applied Real-time Bayesian Analysis in Forecasting Project Cost Overrun｜國立臺灣科技大學博碩士論文系統

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研究生：	劉燕妮 Jenny - Liu
論文名稱：	Applied Real-time Bayesian Analysis in Forecasting Project Cost Overrun Applied Real-time Bayesian Analysis in Forecasting Project Cost Overrun
指導教授：	呂守陞 Sou-Sen Leu
口試委員:	卿建業 none 楊亦東 none
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：	2008
畢業學年度：	96
語文別：	英文
論文頁數：	149
中文關鍵詞：	Cost overrun 、Real-time Bayesian analysis 、Particle filter 、Poisson process
外文關鍵詞：	Cost overrun, Real-time Bayesian analysis, Particle filter, Poisson process
相關次數：	點閱：180 下載：0
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In recent years, cost overrun is become a common problem in the construction industry. Most cost systems often underestimate cost overruns until of a project when there is little that can be done to control the situation. Many factors are necessarily to consider in forecasting cost overruns. Factors such as weather, productivity, material, equipment and management quality are identified escalating cost for the construction projects.
This research proposes a model for predicting cost overrun probability based on the factors that affecting cost escalating. The model assumes a Poisson arrival pattern for cost overrun events occurrence. Real-time Bayesian analysis (particle filter algorithm) is used to run the simulation. Moreover, this research describes the concept of factor combination and sensitivity analysis in order to know most influence factor to cost overrun. The output of the model is presented numerically, providing the early warning of cost overruns to the project manager of the project.

LIST OF CONTENTS

ACKNOWLEDGEMENTS	I
ABSTRACT	III
LIST OF CONTENTS	IV
LIST OF FIGURES	VII
LIST OF TABLES	X
CHAPTER 1 INTRODUCTION	1
1.1 Research Background	1
1.2 Research Objective	2
1.3 Research Scope	3
1.3.1 Boundary Identification	3
1.3.2 Research Assumptions	4
1.4 Research Methodology and Processes	4
1.5 Research Outline	5
CHAPTER 2 LITERATURE STUDY AND REVIEWS	7
2.1 Project Performance and Earn Value Management (EVM)	7
2.2 Study on Project Cost Contingency	12
2.3 Factors Affecting Cost and Duration of Construction Projects	19
2.4 Study on Simulation Process	26
CHAPTER 3 RESEARCH METHODOLOGY	30
3.1 Poisson Process	30
3.1.1 Counting Process	31
3.1.2 Poisson Process	32
3.2 Stochastic Simulation Techniques for Cost Overrun Forecasting	36
3.2.1 Monte Carlo Simulation (MCS)	37
3.2.2 Importance Sampling (IS)	38
3.2.3 Modified Importance Sampling (MIS)	39
3.2.4 Sampling-Importance Resampling (SIR)	40
3.2.5 Markov Chain Monte Carlo (MCMC)	41
3.2.5.1 Metropolis-Hasting Algorithm	41
3.2.5.2 Gibbs Sampler	42
3.2.6 Real-time (on-time) Bayesian Analysis (Particle Filters)	43
3.2.6.1 Introduction to Particle Filters	43
3.2.6.2 Particle Filter Algorithm	44
CHAPTER 4 MODEL CONSTRUCTION AND VERIFICATION	49
4.1 Sequence of Process and Data Collection	49
4.1.1 Process Analysis	49
4.1.2 Data Collection	50
4.2 Factors Affecting Cost Escalating	51
4.3 Poisson Cost Overrun Model	55
4.4 Real-time Bayesian Algorithm	57
4.4.1 Observation Data	57
4.4.2 Real-time Estimation and Prediction	58
4.4.3 Drawing Samples	60
4.5 Adaptive Estimation	65
4.6 Simulated Example	67
CHAPTER 5 REAL CASE STUDY	76
5.1 System of Factors	76
5.1.1 System with in-parallel factors	77
5.1.2 System with series factors	77
5.1.3 System with mixture of in-parallel and series factors	78
5.2 Case Study Analysis	81
5.2.1 Data Collection and Identification	81
5.2.2 Simulation Result	85
5.3 Sensitivity Analysis of System of Factors	93
5.4 Sensitivity Analysis of Changes of Factor	104
5.5 Comparison with EVM	108
CHAPTER 6 CONCLUSION AND FURTHER RESEARCH DIRECTION	113
6.1 Conclusion	113
6.2 Further Research Direction	114
REFERENCES	116
APPENDIX A-1	A-1
APPENDIX A-2	A-3
APPENDIX B-1	B-1
APPENDIX B-2	B-2
APPENDIX B-3	B-3
APPENDIX B-4	B-5
APPENDIX C-1	C-1
APPENDIX C-2	C-2




LIST OF FIGURES

Figure 1.1 Research Process	2
Figure 1.2 Research Scope	3
Figure 1.3 Research Flowchart	6
Figure 2.1 The Model Flowchart	9
Figure 2.2 Structure of Model for Predicting Design Cost Overruns	14
Figure 2.3 Hidden Markov Chain	29
Figure 3.1 Family Trees of Stochastic Simulation Techniques	36
Figure 3.2 Procedure of Metropolis Hasting Algorithm	42
Figure 3.3 Procedure of Gibbs Sampler	43
Figure 3.4 Hidden Markov Chain	44
Figure 3.5 Procedure of Particle Filter	48
Figure 4.1 Sequence of Process Flowchart	50
Figure 4.2 Data Identification Flowchart	51
Figure 4.3 Hidden Markov Chain	56
Figure 4.4 Three span bridge – planned project network	69
Figure 4.5 Real-time Samples of the {λ, μ} and {ν, α} for Weather Factor	71
Figure 4.6 Real-time Samples of the {λ, μ} and {ν, α} for Management Factor	71
Figure 4.7 Real-time Estimates of Cost Overrun (CO) Probability and Cost Overrun (CO) Rate for Weather Factor	72
Figure 4.8 Real-time Estimates of Cost Overrun (CO) Probability and Cost Overrun (CO) Rate for Management Factor	72
Figure 5.1 Vault-tree Model of Cost Overrun “AND” Relationship	77
Figure 5.2 Vault-tree Model of Cost Overrun “AND” Relationship	78
Figure 5.3 Vault-tree Model of Cost Overrun “Mixture in-parallel and series” Relationship	79
Figure 5.4 Real-time Estimates of Cost Overrun Probability and Cost Overrun Rate for Weather, Productivity, Material, Equipment and Management Factor	87
Figure 5.5 Real-time Samples of the {λ, μ} (Left) and {ν, α} (Right) for Weather, Productivity, Material, Equipment and Management Factor	88
Figure 5.5 Real-time Samples of the {λ, μ} (Left) and {ν, α} (Right) for Weather, Productivity, Material, Equipment and Management Factor (Continue)	89
Figure 5.6 Real-time Estimates of Cost Overrun Probability and Cost Overrun Rate for Weather, Productivity, Material, Equipment and Management Factor	91
Figure 5.7 Real-time Samples of the {λ, μ}(Left) and {ν, α} (Right) for Weather, Productivity, Material, Equipment and Management Factor	92
Figure 5.7 Real-time Samples of the {λ, μ}(Left) and {ν, α} (Right) for Weather, Productivity, Material, Equipment and Management Factor (Continue)	93
Figure 5.8 Estimated real-time cost overrun probabilities (left) and cost overrun rates (right) with series systems.	94
Figure 5.9 Estimated real-time cost overrun probabilities (left) and cost overrun rates (right) with series systems.	99
Figure 5.10 Real-time Estimates of Cost Overrun Probability and Cost Overrun Rate of Before New Information Known (Left) and After New Information Known (Right)	105
Figure 5.11 Real-time Estimates of Cost Overrun Probability and Cost Overrun Rate of Before New Information Known (Left) and After New Information Known (Right)	107
Figure 5.12 EVM Predicted Cost versus Planning Cost	109
Figure 5.13 EVM Predicted Cost versus Planning Cost	111























LIST OF TABLES

Table 2.1 Researches on Project Performance and Earn Value Management	8
Table 2.2 Researches on Project Cost Contingency	13
Table 2.3 Contingency – Estimating Methods	19
Table 2.4 Researches about Factors Affecting Cost and Duration of Construction Projects	20
Table 2.4 Researches about Factors Affecting Cost and Duration of Construction Projects (Continue)	21
Table 2.5 Researches on Simulation Process	27
Table 4.1 Description of Identification Value	51
Table 4.2 Identification of Factors that Affecting Cost Escalating	52
Table 4.2 Identification of Factors that Affecting Cost Escalating (Continue)	53
Table 4.3 Factors Identification Matrix	53
Table 4.4 Three Span Bridge – Project Activity Data	68
Table 4.5 Observation Data	70
Table 4.6 Reported Time and Cost Performance of SPECIESS Simulation	74
Table 4.7 SPECIESS Probability Value and PF Probability Value	75
Table 5.1 Factor Combination	80
Table 5.2 Observation Data in Muzha Project	82
Table 5.3 Factors That Affecting Cost Escalating in Muzha Project	82
Table 5.4 Observation Data in Xin Yi Project	83
Table 5.5 Factors That Affecting Cost Escalating in Xin Yi Project	84
Table 5.6 Description of Cost Overrun Probability Converted Value	95
Table 5.7 Particle Filter (PF) Value with Series (OR) and In-parallel (AND) System Compare to the Real Value	95
Table 5.8 Particle Filter (PF) Value from Each Combination	97
Table 5.8 Particle Filter (PF) Value from Each Combination (Continue)	98
Table 5.9 Particle Filter (PF) Value with Series (OR) and In-parallel (AND) System Compare to the Real Value	100
Table 5.10 Particle Filter (PF) Value from Each Combination	102
Table 5.10 Particle Filter (PF) Value from Each Combination (Continue)	103
Table 5.11 Cost Overrun Probability of Before New Information Known and After New Information Known	105
Table 5.12 Cost Overrun Probability of Before New Information Known and After New Information Known	107
Table 5.13 EVM Value, PF Value and Real Value	110
Table 5.14 Estimation of Percentage of Accuracy	110
Table 5.15 EVM Value, PF Value and Real Value	112
Table 5.16 Estimation of Percentage of Accuracy	112

                                

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全文公開日期 2013/01/21 (校內網路)
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