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研究生： Handy Prayogo
Handy Prayogo
論文名稱： Improved Surrogate Models for Reliability Analysis: Classification and Prediction
Improved Surrogate Models for Reliability Analysis: Classification and Prediction
指導教授： 楊亦東
I-Tung Yang
口試委員: 廖國偉
Kuo-Wei Liao
周瑞生
Jui-Sheng Chou
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： 2021
畢業學年度： 109
語文別： 英文
論文頁數： 108
中文關鍵詞： Reliability Based Design Optimization 、Active Learning 、Support Vector Machine 、Symbiotic Organisms Search 、Kriging
外文關鍵詞： Reliability Based Design Optimization, Active Learning, Support Vector Machine, Symbiotic Organisms Search, Kriging
相關次數： 點閱：256  下載：0 
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    • Reliability Based Design Optimization (RBDO) takes into account the uncertainties that lies in the designing process of structures. To model the uncertainties, the major challenge is to reduce the prohibitively great computational expense incurred by the conventional double loop approach, where the design optimization (outer loop) repeatedly calls the reliability analysis of each structural design (inner loop). In the single loop approach, the reliability analysis is replaced with a cheaper approximation. A surrogate model can be utilized to replace the expensive to evaluate function with a cheap approximation. In this study, two new surrogate assisted approach is proposed to improve upon the traditional single loop approach in RBDO. The first approach called SOS-ASVM improves the classification of candidate solutions in terms of their reliability. The performance of SOS-ASVM is validated by comparisons with other popular surrogate-based model such as Support Vector Machine (SVM), Artificial Neural Network (ANN), and Kriging. It is found that the proposed SOS-ASVM framework is more effective and efficient in classifying the feasibility of the design solutions in all the cases. The second approach called Active-learning Kriging with Confidence Sampling (AKCS) predicts the probability of failure for design solutions. For each case, AKCS has been compared with relatively new Kriging-based method, including AFBAM and AK-MCS with different learning functions, namely AK-MCS+EFF, AK-MCS+U, and AK-MCS+H. It is shown that AKCS yields a better and more accurate in the prediction of failure probability than all the other methods, especially when the limit state function is complex and the probability of failure is very low.

    ABSTRACT i ACKNOWLEDGEMENT ii TABLE OF CONTENTS iv LIST OF FIGURES vii LIST OF TABLES ix ABBREVIATIONS AND SYMBOLS x CHAPTER 1: INTRODUCTION 1 1.1 Research Background 1 1.2 Research Objective 5 1.3 Research Outline 5 CHAPTER 2: LITERATURE REVIEW 7 2.1 Formulation of RBDO Problem 7 2.2 Reliability Analysis 9 2.2.1 MPP-based Methods 9 2.2.2 Simulation Methods 10 2.2.3 Surrogate Models 12 2.3 Reliability-based Design Optimization Methods 16 2.3.1 Double-loop Approaches 16 2.3.2 Decoupled Approaches 17 2.3.3 Single-loop Approaches 19 2.4 Summary 20 CHAPTER 3: METHODOLOGY 22 3.1 SOS-ASVM Framework 22 3.1.1 Symbiotic Organisms Search 22 3.1.2 Support Vector Machine 25 3.1.3 Active-learning Support Vector Machine 28 3.1.4 Monte Carlo Simulation 29 3.2 AKCS Method 32 3.2.1 Kriging 32 3.2.2 Active Learning Reliability Method Combining Kriging and Monte Carlo Simulation 34 3.2.3 Learning Function 37 CHAPTER 4: PROPOSED FRAMEWORK 40 4.1 SOS-ASVM Framework 40 4.2 AKCS Method 45 CHAPTER 5: CASE STUDY 65 5.1 SOS-ASVM Framework 65 5.1.1 Experimental Setup 65 5.1.2 Parameter Selection 66 5.1.3 Cantilever Beam 67 5.1.4 Bracket Structure 69 5.1.5 Twenty-five-bar Space Truss 72 5.1.6 Summary 77 5.2 AKCS Method 78 5.2.1 Two-dimensional Nonlinear Problem 79 5.2.2 Dynamic Nonlinear Oscillator 81 5.2.3 Twenty-five-bar Space Truss 83 CHAPTER 6: CONCLUSION 86 6.1 Review the Research Purpose 86 6.2 Conclusions 86 6.3 Future Research Works 88 REFERENCES 89

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