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研究生： Aris Yan Jaya Mendrofa
Aris Yan Jaya Mendrofa
論文名稱： Dynamic Feature Selection for the Prediction of On-site Construction Productivity Using SOS-LSSVM
Dynamic Feature Selection for the Prediction of On-site Construction Productivity Using SOS-LSSVM
指導教授： 鄭明淵
Min-Yuan Cheng
口試委員: 鄭明淵
Min-Yuan Cheng
張陸滿
Luh-Maan Chang
曾惠斌
Hui-Ping Tserng
方亦卓
Yi-Cho Fang
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： 2019
畢業學年度： 107
語文別： 英文
論文頁數： 86
中文關鍵詞： On-site Productivity 、Prediction Model 、Feature Selection 、Symbiotic Organisms Search 、Least Square Support Vector Machine
外文關鍵詞： On-site Productivity, Prediction Model, Feature Selection, Symbiotic Organisms Search, Least Square Support Vector Machine
相關次數： 點閱：296  下載：0 
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    • Productivity is one of the crucial elements for managing construction operations effectively, which has a direct relationship among time and cost. The prediction of productivity is important in order to make the productivity estimate more effective in decision making process for future construction projects. This study aims to identify the input parameters that highly relevant to the on-site productivity in construction project that can achieve the higher accuracy of the prediction model performance. The Symbiotic Organisms Search-Least Square Support Vector Machine with Feature Selection (SOS-LSSVMFS) is developed in order to assist the goal of this study, which incorporates LS-SVM, FS, and SOS. The historical data of real case regarding the on-site construction productivity is presented in order to validate the performance of the proposed model. The performance result obtained is compared to other AI techniques, include BPNN, SVM, ESIM, LS-SVM, and SOS-LSSVM. The results demonstrated that SOS-LSSVMFT is capable to select the highly relevant input variables and increase the performance accuracy of on-site construction productivity prediction model.

    Productivity is one of the crucial elements for managing construction operations effectively, which has a direct relationship among time and cost. The prediction of productivity is important in order to make the productivity estimate more effective in decision making process for future construction projects. This study aims to identify the input parameters that highly relevant to the on-site productivity in construction project that can achieve the higher accuracy of the prediction model performance. The Symbiotic Organisms Search-Least Square Support Vector Machine with Feature Selection (SOS-LSSVMFS) is developed in order to assist the goal of this study, which incorporates LS-SVM, FS, and SOS. The historical data of real case regarding the on-site construction productivity is presented in order to validate the performance of the proposed model. The performance result obtained is compared to other AI techniques, include BPNN, SVM, ESIM, LS-SVM, and SOS-LSSVM. The results demonstrated that SOS-LSSVMFT is capable to select the highly relevant input variables and increase the performance accuracy of on-site construction productivity prediction model.

    ABSTRACT i ACKNOWLEDGEMENT ii TABLE OF CONTENTS iii LIST OF ABBREVIATIONS vi LIST OF SYMBOLS viii LIST OF FIGURES x LIST OF TABLES xi CHAPTER 1. INTRODUCTION 1 1.1 Research Motivation 1 1.2 Research Objective 4 1.3 Scope of Study 5 1.3.1 Research Boundary 5 1.3.2 Assumptions 5 1.4 Research Methodology 5 1.4.1 Introduction 8 1.4.2 Literature Review 8 1.4.3 Model Construction 9 1.4.4 Case Study and Model Implementation 9 1.4.5 Conclusions and Recommendations 10 1.5 Research Outline 10 CHAPTER 2. LITERATURE REVIEW 11 2.1 Productivity in Construction 11 2.2 Factors Affecting On-site Construction Productivity 12 2.3 Construction Productivity Modeling Techniques 16 2.4 Least Square Support Vector Machine (LS-SVM) 17 2.5 Feature Selection 20 2.6 Symbiotic Organisms Search (SOS) 22 2.6.1 Mutualism Phase 23 2.6.2 Commensalism Phase 24 2.6.3 Parasitism Phase 24 CHAPTER 3. SYMBIOTIC ORGANISMS SEARCH - LEAST SQUARE SUPPORT VECTOR MACHINE WITH FEATURE SELECTION (SOS-LSSVMFS) 25 3.1 Model Architecture 25 3.2 Model Application Process 27 CHAPTER 4. CASE STUDY AND IMPLEMENTATION 35 4.1 Case study 35 4.2 Model Validation and Application 39 4.2.1 Data Preprocessing and Cross Validation 39 4.2.2 Parameters Configuration 41 4.2.3 Model Training 41 4.2.4 Model Testing 41 4.3 Result and Comparison 42 4.3.1 Performance Result 42 4.3.2 Performance Comparison 45 4.3.3 Testing Result 49 CHAPTER 5. CONCLUSION AND RECOMMENDATION 52 5.1 Research Purpose Review and Summary 52 5.2 Conclusions 52 5.3 Recommendations 53 REFERENCES 54 APPENDIX A 63

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