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研究生： 阮玉梅
Ngoc-Mai Nguyen
論文名稱： 自然啟發式創新人工智慧最佳化預測技術於土木工程實務 管理之系統研發與應用
Artificial Intelligence Using Novel Metaheuristic Optimization and Predictive Techniques for Civil Engineering and Management
指導教授： 周瑞生
Jui-Sheng Chou
口試委員: 鄭明淵
Min-Yuan Cheng
楊亦東
I-Tung Yang
王維志
Wei-Chih Wang
曾仁杰
Ren-Jye Dzeng
曾惠斌
Hui-Ping Tserng
陳柏翰
Po-Han 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： 2021
畢業學年度： 109
語文別： 英文
論文頁數： 426
中文關鍵詞： 優化演算法 、營建管理 、人工智慧 、機器學習 、鋼筋混凝土 、桥梁冲刷深度
外文關鍵詞： Metaheuristic optimization, Construction engineering and project management, Machine learning, Hybrid ensemble model, Reinforced concrete, Scour depth
相關次數： 點閱：256  下載：0 
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    Artificial Intelligence (AI), big data, and optimization technology have become increasingly innovative and widely used in various industries and cultures, including civil and construction engineering. Although numerous AI-based inference models have been put forward to address various problems, they simply are in the form of three following types: single models, ensemble models, and hybrid models. Along with AI-based inference techniques, metaheuristic optimization algorithms have attracted great interest in recent years for resolving engineering/management-related optimization issues but they have different disadvantages in terms of efficiency, effectiveness, and automation that users must deal with in utilization. The objectives and contributions of this research thus include: (1) developing a novel optimization algorithm, called forensic-based investigation algorithm (FBI) to help engineers/managers to tackle optimization problems with low computational effort and high accuracy. The effectiveness and efficiency of FBI was confirmed through analytical results that demonstrated FBI as being superior to all compared well-known algorithms; (2) developing a metaheuristic optimization platform to provide performance indicators clearly, logically, and graphically. Moreover, it is a reliable system to benchmark a new proposed optimization algorithm in the future; and (3) establishing a novel type of AI-inference technique, presented in two independent systems: metaheuristic-optimized ensemble system (MOES) and metaheuristic-optimized stacking system (MOSS) - for civil and construction engineering management. Both MOES and MOSS are powerful AI approaches with remarkably greater accuracy than current AI techniques because they combine the advantages of hybrid model and ensemble model. MOES was hybridized an optimizer and a homogeneous ensemble model, while MOSS was hybridized an optimizer and a heterogeneous ensemble model. The FBI algorithm was integrated in MOES and MOSS to simultaneously find the optimal values of all hyper-parameters of constituent AI models to generate the most effective ensemble systems. The MOES was applied to support structural engineers in achieving accurate estimations of the mechanical strength of reinforced concrete (RC) materials with four real case studies. Meanwhile, MOSS was applied to assist civil engineers in accurately estimating scour depth at bridge piers. The efficiency of the MOSS is verified comprehensively using three case studies of both laboratory data and field data that cover various levels of complexity and types of pier foundations in reality. The performances of MOES and MOSS were compared to those of other single AI models, conventional ensemble AI models, hybrid models, and empirical methods. The analytical results of a cross-validation method revealed that MOES and MOSS were the most reliable approaches, achieving the best performance evaluation metrics with the lowest prediction errors. Additionally, both MOES and MOSS are user-friendly tools because they can run automatically with support of the FBI in setting all control hyper-parameters.

    TABLE OF CONTENTS ABSTRACT i ACKNOWLEDGEMENTS iii TABLE OF CONTENTS iv LIST OF FIGURES vii LIST OF TABLES viii CHAPTER 1: INTRODUCTION 1 1.1 Research Background and Motivations 1 1.2 Research Objectives 2 1.3 Dissertation Organization 4 CHAPTER 2: LITERATURE REVIEW 5 2.1 Metaheuristic Optimization Algorithms 5 2.2 Machine Learning Inference Models 8 CHAPTER 3: FORENSIC BASED INVESTIGATION ALGORITHM 12 3.1 Forensic Investigation Process 12 3.2 Development of Metaheuristic Optimization Algorithm 14 3.3 FBI Validation 21 3.3.1 50 Common Benchmark Functions 22 3.3.2 CEC Benchmark Functions 31 3.3.3 FBI Component Analysis in Solving High-Dimensional Problems 34 3.4 Advantages of FBI 36 CHAPTER 4: GENERAL FRAMEWORK OF ENSEMBLE MACHINE MEARNING MODELS 38 4.1 Single Machine Learning Models 38 4.1.1 ANN and RBFNN 38 4.1.2 SVR and LSSVR 39 4.2 Ensemble Machine Learning Models 41 4.2.1 Homogeneous Ensemble Model 41 4.2.2 Heterogeneous Ensemble Model 42 4.3 Metaheuristics-Optimized Homogeneous Ensemble System: MOES 42 4.3.1 General Concept of MOES 42 4.3.2 Development of MOES 44 4.3.3 Performance evaluation metrics 48 4.4 Metaheuristics-Optimized Heterogeneous Ensemble System: MOSS 49 4.4.1 General Concept of MOSS 49 4.4.2 Development of MOSS 51 CHAPTER 5: ENGINEERING APPLICATIONS OF FBI ALGORITHM 55 5.1 Resource-Constrained Project Scheduling Problem 55 5.2 Metaheuristic Optimization Platform 58 CHAPTER 6: ENGINEERING APPLICATIONS OF MOES AND MOSS 61 6.1 MOES for Predicting Mechanical Strength of Reinforced Concrete Materials 61 6.1.1 Data Description 62 6.1.1.1 Load Capacity of RC Continuous Deep Beams 62 6.1.1.2 Shear Strength of RC Deep Beams and RC Beams with FRP Reinforcement 64 6.1.1.3 Torsional Strength of RC Beams 66 6.1.2 Experimental Results and Discussions 67 6.1.2.1 Load Capacity of RC Continuous Deep Beams 67 6.1.2.2 Shear strength of RC deep beams and RC beams with FRP reinforcement 70 6.1.2.3 Torsional strength of RC beams 74 6.2 MOSS for Predicting Scour Depth at Bridge Piers 77 6.2.1 Data Description 79 6.2.2 Experimental Results and Discussions 81 6.2.2.1 MOSS performance 81 6.2.2.2 Performance comparison 82 6.2.2.3 Optimal hyperparameters of RBFNN and LSSVR 89 CHAPTER 7: CONCLUSIONS AND RECOMMENDATIONS 92 RERERENCES 96 APPENDIX A: CODING 113 A.1 FBI MATLAB Code 113 A.2 FBI Python Code 132 A.3 Metaheuristic Optimization Platform 138 A.4 K-folds Crossing Validation 222 A.5 FBI_LSSVR 223 A.6 FBI_RBFNN 245 A.7 MOES 260 A.8 MOSS 291 APPENDIX B: TUTORIALS 323 B.1 FBI 323 B.1.1 FBI Tutorial 1: FBI MATLAB Code 323 B.1.2 FBI Tutorial 2: Benchmark Functions 329 B.1.3 FBI Tutorial 3: Platform and RCPSP 336 B.2 MOES Introduction 342 B.3 MOSS Introduction 350 B.4. K-fold Crossing Validation 357 B.5 FBI_LSSVR Tutorial 359 B.6 FBI_RBFNN Tutorial 365 B.7 MOES MATLAB Code Tutorial 371 B.8 MOSS MATLAB Code Tutorial 377 APPENDIX C: DATASETS 383 C.1 Mechanical Strength of Reinforced Concrete Materials 383 C.1.1 Load Capacity of RC Continuous Deep Beams 383 C.1.2 Shear Strength of RC Deep Beams 385 C.1.3 Shear Strength of RC Beams with FRP Reinforcement 390 C.1.4 Torsional Strength of RC Beams 395 C.2 Scour Depth at Bridge Piers 397 C.2.1 Laboratory Dataset 397 C.2.2 Field Dataset 401 C.2.3 Scour Depth at Complex Pier Foundations 406

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