HYBRID SWARM INTELLIGENCE-BASED SYSTEM TO EVALUATE FORECASTING APPLICATIONS IN CIVIL ENGINEERING AND MANAGEMENT

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研究生：	Anh-Duc Pham Anh-Duc Pham
論文名稱：	HYBRID SWARM INTELLIGENCE-BASED SYSTEM TO EVALUATE FORECASTING APPLICATIONS IN CIVIL ENGINEERING AND MANAGEMENT HYBRID SWARM INTELLIGENCE-BASED SYSTEM TO EVALUATE FORECASTING APPLICATIONS IN CIVIL ENGINEERING AND MANAGEMENT
指導教授：	周瑞生 Jui-Sheng Chou
口試委員:	曾惠斌 Hui-Ping Tserng 陳柏翰 Po-Han Chen 周建成 Chien-Cheng Chou 鄭明淵 Min-Yuan Cheng 楊亦東 I-Tung Yang
學位類別：	博士 Doctor
系所名稱：	工程學院 - 營建工程系 Department of Civil and Construction Engineering
論文出版年：	2014
畢業學年度：	102
語文別：	英文
論文頁數：	170
中文關鍵詞：	civil engineering and management 、artificial intelligence 、firefly algorithm 、chaotic maps 、random walk 、swarm intelligence 、predictive techniques
外文關鍵詞：	civil engineering and management, artificial intelligence, firefly algorithm, chaotic maps, random walk, swarm intelligence, predictive techniques
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Advanced data mining (DM) techniques are potential tools for solving civil engineering and management (CEM) problems.This study investigated the potential use of various advanced approaches and proposes a novelsmart artificial firefly colony algorithm (SAFCA)-based support vector regression models (SAFCAS) that integrates firefly algorithm (FA), chaotic maps, adaptive inertia weight, Levy flights, and support vector machine-based regression (SVR). Firstly, adaptive approach and randomization methods are incorporated in FA to construct a novel and highly effective meta-heuristic algorithm for global optimization. The enhanced FA is then used to identify the optimal set of turning parameters in SVR model. The proposed system is validated by comparing the performance of the SAFCAS with those of empirical methods, well-known AI models, and previous works via cross-validation algorithm and hypothesis test.For real-world engineering cases, eight datasets are collected from reliable laboratories and published literature. Experimental results obtained from theSAFCASconfirm that using the proposed hybrid system in advanced DM approaches significantly improve the accuracy of forecasting methods used to solve real-life CEM problems.

ABSTRACT	i
ACKNOWLEDGEMENTS	ii
ABBREVIATION	viii
LIST OF FIGURES	x
LIST OF TABLES	xii
CHAPTER I: INTRODUCTION	1
1.1 Research Motivations	1
1.2 Research Objectives	3
1.3 Research Scope	4
1.4 Dissertation Structure	4
CHAPTER II: LITERATURE REVIEW	5
2.1 Data Mining and Artificial Intelligence-based Approaches	5
2.2 Hybrid Computational Model	11
2.3 Hybrid Swarm Intelligence Approach in Modeling and Optimization	13
2.4 Chapter Summary	15
CHAPTER III: RESEARCH METHODOGOLY	16
3.1 Overview of Research Methodology	16
3.2 Data Mining Algorithms	16
3.2.1 Artificial Neural Network (ANN)	17
3.2.2 Classification and Regression Trees (CART)	19
3.2.3 Chi-squared Automatic Interaction Detector (CHAID)	20
3.2.4 Generalized Linear Model (GENLIN)	20
3.2.5 Support Vector Machine-based Regression (SVR)	21
3.2.6 Ensemble Model	26
3.3 Hybrid Swarm Intelligence in Optimization Technique	30
3.3.1 Overview of Nature-Inspired Algorithms	31
3.3.2 Smart Components	34
3.3.2.1 Chaotic Maps	34
3.3.2.2 Adaptive Inertia Weight	35
3.3.2.3 Levy Flights	36
3.3.2.4 Combination of Smart Components with Nature-Inspired Algorithm	37
3.3.3 Benchmark Functions	40
3.3.4 Hybrid Swarm Intelligence System	44
3.4 Performance Evaluation Methods	46
3.4.1 Performance Measure	46
3.4.2 Cross-fold Validation Algorithm	47
3.5 Hypothesis Testing	48
3.6 Data Collection and Preprocessing	49
3.6.1 Data Collection	49
3.6.2 Normalization of the Data	50
CHAPTER IV: HYBRID SWARM INTELLIGENCE-BASED SYSTEM CONSTRUCTION	51
4.1 System Architecture	51
4.2 Hybrid Swarm Intelligence-based SVR Model Process	52
4.2.1 A Novel Smart Artificial Firefly Colony Algorithm	52
4.2.2 Hybrid SAFCA-based SVR Model	53
4.3 Proposed System Validation	57
4.3.1 Case No. 1 –The SAFCAS Model for Construction Material Science	57
4.3.1.1 Problem Statement: HPC Compressive Strength	57
4.3.1.2 Concrete Compressive Strength Database	59
4.3.1.3 System Validation	61
4.3.2 Case No. 2 –The SAFCAS Model for Bridge Foundation Design and Maintenance	65
4.3.2.1 Problem Statement: Bridge Scour Depth	65
4.3.2.2 Conventional Regression Methods	66
4.3.2.3 Bridge Scour Database	72
4.3.2.4 System Validation	73
4.3.3 Case No. 3 – The SAFCAS Model for Pavement Structure Design	79
4.3.3.1 Problem Statement: Resilient Modulus of Sub-grade Soils	79
4.3.3.2 Resilient Modulus Formulae	82
4.3.3.3 Resilient Modulus Database	83
4.3.3.4 System Validation	84
4.3.4 Case No.4 – The SAFCAS Model for Building Energy Efficiency	86
4.3.4.1 Problem Statement: Cooling and Heating Loads	86
4.3.4.2 Heating and Cooling Loads Database	89
4.3.4.3 System Validation	91
4.4 Summary of Proposed System	92
CHAPTER V: CONCLUSIONS AND RECOMMENDATIONS	93
5.1 Conclusions	93
5.2 Research Contributions	95
5.3 Future Research Direction and Recommendations	96
REFERENCES	97
APPENDIX A. Analytical results of benchmark functions	113
APPENDIX B. Performance measure via cross-fold for HPC compressive strength (Dataset 1) using SAFCAS – Test dataset.	122
APPENDIX C. Performance measure via cross-fold for HPC compressive strength (Dataset 2) using SAFCAS.	123
APPENDIX D. Performance measure via cross-fold for HPC compressive strength (Dataset 3) using SAFCAS – Test dataset.	124
APPENDIX E.1. Performance measure via cross-fold for scour around abutment-semicircular (Dataset 4a) using individual AI models and ensemble model – Test dataset.	125
APPENDIX E.2. Performance measure via cross-fold for scour around abutment-semicircular (Dataset 4a) using SAFCAS – Test dataset.	126
APPENDIX E.3. Performance measure via cross-fold for scour around abutment-vertical-wall (Dataset 4b) using individual AI models and ensemble model (ANN+CHAID) – Test dataset.	127
APPENDIX E.4. Performance measure via cross-fold for scour around abutment-vertical-wall (Dataset4b) using SAFCAS – Test dataset.	128
APPENDIX E.5. Performance measure via cross-fold for scour around abutment-wing-wall (Dataset 4c) using individual AI models and ensemble model (ANN+CHAID) – Test dataset.	129
APPENDIX E.6. Performance measure via cross-fold for scour around abutment-wing-wall (Dataset 4c) using SAFCAS – Test dataset.	130
APPENDIX F.1. Performance measure via cross-fold for scour around pier (Laboratory data – Dataset 5) using individual AI models and ensemble model (CART+CHAID) – Test dataset.	131
APPENDIX F.2. Performance measure via cross-fold for scour around pier (Laboratory data – Dataset 5) using SAFCAS – Test dataset.	132
APPENDIX G.1. Performance measure via cross-fold for scour around pier (Field-US & Ohio) (Dataset 6) using individual AI models and ensemble model (ANN+CART+CHAID) – Test dataset.	133
APPENDIX G.2. Performance measure via cross-fold for scour around pier (Field-US & Ohio) (Dataset 6) using SAFCAS – Test dataset.	134
APPENDIX H.1. Performance measure via cross-fold for resilient modulus (Dataset 7) using individual AI models and ensemble model (CART+CHAID) – Training dataset.	135
APPENDIX H.2. Performance measure via cross-fold for resilient modulus (Dataset 7) using individual AI models and ensemble model (CART+CHAID) – Test dataset.	136
APPENDIX H.3. Performance measure via cross-fold for resilient modulus (Dataset 7) using SAFCAS – Test dataset.	137
APPENDIX I.1. Performance measure via cross-fold for cooling and heating load (Dataset 8a - Cooling) using SAFCAS – Test dataset.	138
APPENDIX I.2. Performance measure via cross-fold for cooling and heating load (Dataset 8b - Heating) using SAFCAS – Test dataset.	139
APPENDIX J. MATLAB code.	140
CURRICULUM VITAE	158

                                

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