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研究生: Dac-Khuong Bui
Dac-Khuong Bui
論文名稱: Nature-Inspired Metaheuristic Support Vector Regression System for Civil Engineering Managers
Nature-Inspired Metaheuristic Support Vector Regression System for Civil Engineering Managers
指導教授: 周瑞生
Jui-Sheng Chou
口試委員: 楊國鑫
Kuo-Hsin Yang
曾惠斌
Hui-Ping Tserng
蔡志豐
Chih-Fong Tsai
陳榮河
Rong-Her 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: 2015
畢業學年度: 103
語文別: 英文
論文頁數: 172
外文關鍵詞: expert computing system, nature-inspired metaheuristics, civil engineering.
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  • Developing an expert system has been considered as complex and knowledge driven process. This study proposes a nature-inspired metaheuristic regression system that can find appropriate solutions. The system uses a graphical user interface but does not require a mathematical program installation. The user-friendly interface was designed in Matlab GUIDE and was implemented by Matlab compiler. The standalone system is easy to use and has many functions, including evaluation: use opened data file, select test set, hold-out, cross validation and prediction to solve many civil engineering problems with simple manipulations on the interface of system. Five benchmark functions were used to evaluate the effectiveness of the optimization approach. The performance of proposed system was then validated by comparing its solutions obtained for civil engineering problems with those obtained by empirical methods reported previously. Five actual data sets including energy-efficient buildings, construction material strength, concrete structure shear strength, bridge scour depth, and sub base soil modulus were used as case studies. The prediction accuracy were 8.24% – 91.76% better than those of previously reported models. The analytical results support the feasibility of using the proposed system to solve civil engineering problems. The system was also much faster at identifying the optimum parameters and solving problems. The experiments confirmed that the novel nature-inspired metaheuristic regression system proposed in this study has superior efficiency, effectiveness, and accuracy.

    ABSTRACT i TABLE OF CONTENTS v LIST OF FIGURES viii LIST OF TABLES ix ABBREVIATIONS AND SYMBOLS x Chapter 1 Introduction 1 1.1 Research background 1 1.2 Research objectives 3 1.3 Research process 4 Chapter 2 Literature review 5 2.1 Current practice of artificial intelligence in civil engineering 5 2.2 Hybrid artificial intelligence model 6 2.3 Development of expert system in artificial intelligence 8 Chapter 3 Methodology 9 3.1 SFA-LSSVR model 9 3.1.1 Support vector machine for regression 9 3.1.2 Novel smart artificial firefly 11 3.1.3 Constructing the Smart Artificial Firefly based LSSVR model 16 3.2 Optimization algorithm evaluation 18 3.3 System design and analysis 19 3.4 System performance evaluation methods 24 3.5 Hypothesis Testing 25 Chapter 4 System development 27 4.1 System requirement and implementation 27 4.2 System architecture 28 4.3 System interface design 30 Chapter 5 System validation 32 5.1 Benchmarking optimization algorithm 32 5.2 Case study 34 5.2.1 Case 1: building cooling load 36 5.2.2 Case 2: high-performance concrete compressive strength 38 5.2.3 Case 3: predicting shear strength of reinforced concrete deep beams 39 5.2.4 Case 4: bridge scour depth 41 5.2.5 Case 5: resilient modulus of sub-grade soils 42 5.3 Analysis remarks 43 Chapter 6 Conclusions 47 References 51 APPENDIX A. User interface snapshot 60 A.1 Main user interface 60 A.2 SFA-LSSVR interface (evaluation) 61 A.3 SFA-LSSVR interface (prediction) 62 A.4 LSSVR interface (evaluation) 63 A.5 LSSVR interface (prediction) 64 APPENDIX B. Original dataset 65 B.1 Original dataset for cooling load (dataset 1) 65 B.2 Original dataset for HPC compressive strength (dataset 2) 87 B.3 Original dataset for reinforced concrete deep beams (dataset 3) 119 B.4 Original dataset for bridge scour depth (dataset 4) 125 B.5 Original dataset for resilient modulus of subgrade soil (dataset 5) 128 APPENDIX C. Performance measure via cross-fold using the nature-inspired metaheuristic regression system. 133 C.1 Performance measure via cross-fold for cooling load (dataset 1) 133 C.2 Performance measure via cross-fold for HPC compressive strength (dataset 2). 134 C.3 Performance measure via cross-fold for reinforced concrete deep beams (dataset 3). 135 C.4 Performance measure via cross-fold for bridge scour depth (dataset 4). 136 C.5 Performance measure via cross-fold for resilient modulus of subgrade soil (dataset 5). 137 APPENDIX D. Analysis report 138 D.1 Analysis report for cooling load case (dataset 1) 138 D.2 Analysis report for HPC compressive strength case (dataset 2). 145 D.3 Analysis report for reinforced concrete deep beams case (dataset 3). 152 D.4 Analysis report for bridge scour depth case (dataset 4). 159 D.5 Analysis report for resilient modulus of subgrade soil case (dataset 5). 166

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