臺灣博碩士論文加值系統

Design activities in a construction project are often carried out through an iterative process. Iteration design processes improve the quality and the function of design. More specifically in complex projects, iterations play an important role for continuity of project and completion of the project. Iterative process modeling gives better understanding to estimate the project duration and make trade-off between design quality with reworks or changes. This thesis describes a probabilistic model based on iteration and dependencies among the activities that can be improved to obtain faster and better design process. The proposed model is result of combination between Design Structure Matrix (DSM) and Hidden Markov Model (HMM). This model imitates the real condition in a project by updating the observation through checklist and questionnaire to obtain the parameter in Hidden Markov Model that were analyzed by expert judgment method. The model is performed under several conditions and is compared with previous research to validate the general concept. The results show that DSM-HMM model can describe the design process in detail: number of iterations, quality of the design, and total duration of the activity. This model can improve the design process and can be applied in the practical design in construction projects.

Design activities in a construction project are often carried out through an iterative process. Iteration design processes improve the quality and the function of design. More specifically in complex projects, iterations play an important role for continuity of project and completion of the project. Iterative process modeling gives better understanding to estimate the project duration and make trade-off between design quality with reworks or changes. This thesis describes a probabilistic model based on iteration and dependencies among the activities that can be improved to obtain faster and better design process. The proposed model is result of combination between Design Structure Matrix (DSM) and Hidden Markov Model (HMM). This model imitates the real condition in a project by updating the observation through checklist and questionnaire to obtain the parameter in Hidden Markov Model that were analyzed by expert judgment method. The model is performed under several conditions and is compared with previous research to validate the general concept. The results show that DSM-HMM model can describe the design process in detail: number of iterations, quality of the design, and total duration of the activity. This model can improve the design process and can be applied in the practical design in construction projects.

TABLES OF CONTENTS
DEDICATION.... i
ACKNOWLEDGEMENTS ii
ABSTRACT....... iii
TABLES OF CONTENTS iv
LIST OF FIGURES vii
LIST OF TABLES x
CHAPTER 1 INTRODUCTION 1
1.1 Research Background 1
1.2 Research Objectives 3
1.3 Research Scope 4
1.4 Research Methodology 6
1.5 Research Outline 6
CHAPTER 2 LITERATURE REVIEW 8
2.1 Overview of Design Structure Matrix Concept 8
2.2 Hidden Markov Model Concept 14
2.2.1 Evaluation Problem 15
2.2.2 Decoding Problem 16
2.2.3 Learning Problem 16
2.3 Summary 18
CHAPTER 3 RESEARCH METHODOLOGY 19
3.1 Research Flow Methodology 19
3.2 Reason for Adopting Hidden Markov Model Concept 20
3.3 Viterbi Algorithm and Iteration Concept 22
3.4 Questionnaire 28
3.5 Summary 30
CHAPTER 4 MODELLING AND VALIDATION 32
4.1 Hybrid HMM-DSM Model Architectural 32
4.1.1 Engineering Design Checklist 34
4.1.2 HMM and Viterbi Algorithm Modification 35
4.2 Model Verification and Iteration Stopping Criteria 36
4.3 Transformation HMM to DSM 41
4.4 Model Validation 43
4.5 Analysis 51
4.6 Sensitivity Analysis 58
4.6.1 Initial Probability 58
4.6.2 Transition Probability 59
4.6.3 Emission Probability 63
4.7 Questionnaire Calculation Procedure 65
4.8 Summary 67
CHAPTER 5 MODEL SIMULATION UNDER DIFFERENT SCENARIOS AND OPTIMIZATION 69
5.1 Model Simulation Based on Constant Observation 69
5.1.1 Model Simulation with Updated Observation in Good Condition 76
5.1.2 Model Simulation with Updated Observation in Average Condition 84
5.1.3 Model Simulation with Updated Observation in Bad Condition 85
5.2 Model Simulation Based on Random Observation 86
5.2.1 Model Simulation with Updated Observation Follow Triangular Distribution 88
5.2.2 Model Simulation with Updated Observation Follow Weibull Distribution 92
5.3 Summary 95
CHAPTER 6 CONCLUSIONS 97
6.1 Conclusions 97
6.2 Future Research Direction 99
REFERENCES 100

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