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研究生： 黃日德
Hoang - Nhat Duc
論文名稱： ESTIMATE AT COMPLETION USING TIME-DEPENDENT EVOLUTIONARY FUZZY SUPPORT VECTOR MACHINE INFERENCE MODEL
ESTIMATE AT COMPLETION USING TIME-DEPENDENT EVOLUTIONARY FUZZY SUPPORT VECTOR MACHINE INFERENCE MODEL
指導教授： 鄭明淵
Min-Yuan Cheng
口試委員: 陳鴻銘
Hung-Ming Chen
潘南飛
Nang-Fei Pan
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： 2010
畢業學年度： 98
語文別： 英文
論文頁數： 106
中文關鍵詞： Time Series Forecasting 、Fuzzy Logic 、weighted Support Vector Machine 、Estimate at Completion
外文關鍵詞： Estimate at Completion, weighted Support Vector Machine, Fuzzy Logic, Time Series Forecasting
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    • In construction management, successful in cost control during construction stage is critical to the general contractor survival. The reason is that the bottom line of construction management is to ensure the project carried out within the planned budget. Cost overrun may lead to profit damage, occasionally even bring about project failure. To deal with such issue, this research utilizes Estimate at Completion (EAC) and Time-dependent Evolutionary Fuzzy Support Vector Machine (EFSIMT) to form the model (EAC-EFSIMT) uniquely for EAC prediction. In EAC-EFSIMT, the Support Vector Machines is utilized as a supervised learning instrument, in order to infer the causal relationship between multiple attributes in the input space and EAC as the single output in the output space. The fuzzy logic is used to emphasize the approximate reasoning. Moreover, to address the feature of time-dependent data, the inference model employs 3 types of time series functions (Linear, Quadratic, and Exponential) to weight training data points. The effect of each time series function on the model performance is investigated individually. This research work has proved that integration of time series function can meliorate the outcome of EAC prediction. Through the training, testing and results comparison process, the Exponential function has been identified as the preferable time series function for EAC problem. Moreover, the capability of EAC-EFSIMT in real-world situation is demonstrated. It is shown that newly proposed model is an effective replacement for previous methods in construction project cost control.

    In construction management, successful in cost control during construction stage is critical to the general contractor survival. The reason is that the bottom line of construction management is to ensure the project carried out within the planned budget. Cost overrun may lead to profit damage, occasionally even bring about project failure. To deal with such issue, this research utilizes Estimate at Completion (EAC) and Time-dependent Evolutionary Fuzzy Support Vector Machine (EFSIMT) to form the model (EAC-EFSIMT) uniquely for EAC prediction. In EAC-EFSIMT, the Support Vector Machines is utilized as a supervised learning instrument, in order to infer the causal relationship between multiple attributes in the input space and EAC as the single output in the output space. The fuzzy logic is used to emphasize the approximate reasoning. Moreover, to address the feature of time-dependent data, the inference model employs 3 types of time series functions (Linear, Quadratic, and Exponential) to weight training data points. The effect of each time series function on the model performance is investigated individually. This research work has proved that integration of time series function can meliorate the outcome of EAC prediction. Through the training, testing and results comparison process, the Exponential function has been identified as the preferable time series function for EAC problem. Moreover, the capability of EAC-EFSIMT in real-world situation is demonstrated. It is shown that newly proposed model is an effective replacement for previous methods in construction project cost control.

    ABSTRACT........I ACKNOWLEDGEMENTS........III TABLE OF CONTENTS.......IV ABBREVIATIONS AND SYMBOLS.......VIII LIST OF FIGURES.........XII LIST OF TABLES..........XV CHAPTER 1 INTRODUCTION..........1 1.1 Research Motivation.........1 1.2 Research Objectives.........5 1.3 Scope Definition and Basic Assumption.......6 1.4 Research Methodology........6 1.4.1 Problem Formulation.......7 1.4.2 EAC Forecasting Model.......7 1.4.3 EAC Prediction and Result Comparison.......7 1.4.4 Model Application.......8 1.5 Study Outline.......8 CHAPTER 2 LITERATURE REVIEW.......12 2.1 Estimate at Completion.......13 2.2 Time Series Forecasting Techniques.......17 2.3 Fuzzy Logic.......23 2.4 Weighted Support Vector Machine.......25 2.5 Fast Messy Genetic Algorithm.......29 CHAPTER 3 ESTIMATE AT COMPLETION USING TIME-DEPENDENT EVOLUTIONARY FUZZY SUPPORT VECTOR MACHINE INFERENCE MODEL FRAMEWORK.......32 3.1 Training Data.......32 3.2 Weighting Data Using Time Series Functions.......37 3.3 Fuzzification.......40 3.4 Weighted Support Vector Machine.......40 3.5 Defuzzification.......41 3.6 fmGA Parameter Searching.......42 3.7 Fitness Evaluation.......45 3.8 Termination Criteria....... 46 3.9 Optimal Prediction Model.......46 CHAPTER 4 ESTIMATE AT COMPLETION AND RESULT COMPARISON.......48 4.1 Training Process.......49 4.1.1 Training Process Using Linear Time Series Function....... 50 4.1.2 Training Process Using Quadratic Time Series Function.......54 4.1.3 Training Process Using Exponential Time Series Function.......57 4.1.4 Summary of Training Error.......60 4.2 Testing Process.......61 4.2.1 Training Process on Dataset L.......61 4.2.2 Training Process on Dataset M.......68 4.3 Result Comparison.......70 4.3.1 Comparison between EAC-EFSIMT and Traditional Methods....... 70 4.3.2 Comparison between EAC-EFSIMT and ESIM.......72 CHAPTER 5 APPLICATION OF ESTIMATE AT COMPLETION USING TIME- DEPENDENT EVOLUTIONARY FUZZY SUPPORT VECTOR MACHINE INFERENCE MODEL.......79 5.1 Utilization of EAC-EFSIMT in Construction Project.......79 5.1.1 Application of EAC for Various Stages in Construction Project.......79 5.1.2 Project Cost Influencing Indices Analysis.......80 5.1.3 Project Cost Diagram Analysis.......84 5.2 Application of EAC-EFSIMT for Actual Project.......84 CHAPTER 6 CONCLUSION.......90 6.1 Review of Research Purpose.......90 6.2 Summary.......91 6.3 Conclusion.......92 6.4 Future Recommendations.......93 REFERENCE.......95 APPENDIX.......A-1

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