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研究生： 高明秀
Minh-Tu Cao
論文名稱： Artificial Intelligence-Based Inference Support Models for Construction Engineering and Management
Artificial Intelligence-Based Inference Support Models for Construction Engineering and Management
指導教授： 鄭明淵
Min-Yuan Cheng
口試委員: 黃榮堯
Rong-yau (Ethan) Huang
王維志
Wei-Chih Wang
楊亦東
I-Tung Yang
柯千禾
Chien-Ho Ko
陳維東
Wei Tong Chen
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： 2015
畢業學年度： 103
語文別： 英文
論文頁數： 132
外文關鍵詞： construction engineering and management, inference support system, fuzzy logic.
相關次數： 點閱：377  下載：1 
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    • Problems in the field of construction management are sophisticated, highly uncertain, and context-dependent. Therefore, using artificial intelligence (AI) to tackle these problems is a promising direction for research. The present research integrates Multivariate Adaptive Regression Splines (MARS), Radial Basis Function Neural Network (RBFNN), Artificial Bee Colony (ABC), and Fuzzy Logic (FL) to develop three novel inference models. The first model, the Evolutionary Multivariate Adaptive Regression Splines (EMARS), incorporates MARS and ABC. The EMARS resolves regression problems using input values for the underlying function mapping response and the various factors of influence that are provided by civil engineers. The second model, the Self-adaptive Structure Radial Basis Function Inference Model (SSRIM), fuses MARS, RBFNN, and ABC. The SSRIM is an efficient model for addressing inference tasks that have many un-assessed potential factors of influence. After the MARS removes the redundant (neutral) factors from the set of input variables, the ABC-optimized RBFNN uses the remaining input variables to execute the supervised learning task. Finally, the Intelligent Fuzzy Radial Basis Function Neural Network Inference Model (IFRIM), hybridizes RBFNN, FL, and ABC. In the IFRIM, FL handles vague input information, RBFNN handles the fuzzy input-output mapping relationships, and the ABC search engine employs optimization to identify the most suitable tuning parameters for RBFNN and FL based on minimal error estimation. Experimental results obtained from the applications of these newly established AI models demonstrate that these models may significantly enhance the ability of decision makers to resolve problems in the field of construction engineering and management.

    TABLE OF CONTENTS ABSTRACT ii ACKNOWLEDGEMENTS iii ABBREVIATIONS AND SYMBOLS vi LIST OF FIGURES x LIST OF TABLES xi CHAPTER 1. INTRODUCTION 1 1.1 Research Motivation 1 1.2 Research Objectives 5 1.3 Research Scope 6 1.3.1 Boundary Identification 6 1.3.2 Research Assumption and Hypotheses 7 1.4 Research Organization 7 CHAPTER 2. LITERATURE REVIEW 12 2.1 Multivariate Adaptive Regression Splines (MARS) 12 2.2 Radial Basis Function Neural Network (RBFNN) 14 2.3 Fuzzy Logic (FL) 16 2.4 Artificial Bee Colony (ABC) 19 CHAPTER 3. MODEL CONSTRUCTION 22 3.1 Evolutionary Multivariate Adaptive Regression Splines (EMARS) 22 3.2 Self-adaptive Structure Radial Basis Function Inference Model (SSRIM) 25 3.3 Intelligent Fuzzy Radial Basis Function Neural Network (IFRIM) 27 3.4 Models’ Limitations 32 3.5 Potential Application Areas 32 CHAPTER 4. CASE STUDY AND MODEL VALITDATON 33 4.1 Evaluation Performance Methods 33 4.2 Case 1 - Strength of Rubberized Concrete 34 4.2.1 Problem statement 34 4.2.2 Data collection and process 37 4.2.3 System validation 39 4.3 Case 2 - Shear Strength in Reinforced-Concrete Deep Beams 43 4.3.1 Problem statement 43 4.3.2 Data collection and process 45 4.3.3 System validation 49 4.4 Case 3 - Building Energy Performance 56 4.4.1 Problem statement 56 4.4.2 Data collection and process 58 4.4.3 System validation 61 4.5 Case 4 - Uplift Capacity of Suction Caissons 70 4.5.1 Problem statement 70 4.5.2 Data collection and process 72 4.5.3 System validation 73 4.6 Case 5 - Equilibrium Scour Depth at Bridge Piers 82 4.6.1 Problem statement 82 4.6.2 Data collection and process 84 4.6.3 System validation 86 4.7 Case 6 - Taiwan Construction Cost Index 91 4.7.1 Problem statement 91 4.7.2 Data collection and process 93 4.7.3 System validation 95 CHAPTER 5. CONCLUSIONS AND RECOMMENDATIONS 105 5.1 Conclusions 105 5.2 Research Contributions 108 5.3 Future Research Direction and Recommendations 109 REFERENCES 110

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