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研究生: 范育祺
Abdoulie Fatty
論文名稱: Rock Slope Back Calculations Considering Material Uncertainties
Rock Slope Back Calculations Considering Material Uncertainties
指導教授: 李安叡
An-Jui Li
口試委員: 林宏達
Horn-Da Lin
楊亦東
I-Tung Yang
熊彬成
Benson Hsiung
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: 2019
畢業學年度: 107
語文別: 英文
論文頁數: 107
中文關鍵詞: Back CalculationGenetic AlgorithmsParticle Swarm OptimizationRock Slopes
外文關鍵詞: Back Calculation, Genetic Algorithms, Particle Swarm Optimization, Rock Slopes
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  • Generally, in Geotechnical engineering, back analyses are used to investigate uncertain parameters. Back analyses can be done by considering known conditions, such as failure surfaces, displacements, and structural performances. In fact, solutions of many engineering problems can be formulated as the optimized results of a function. While a continuous convex optimization process governs many engineering problems, this is not the case for most Geotechnical problems. Many Geotechnical problems have irregular solution domains, with the objective function being nonconvex and may not be a continuous function. As such, a complex nonlinear optimization function is typically required for most Geotechnical problems in order to attain better understandings of these uncertainties. Therefore, Particle Swarm Optimization (PSO) and Genetic Algorithm (GA) are utilized in this thesis to facilitate in back analyses based on upper bound finite element limit analysis method. These techniques are part of evolutionary computation, which is appropriate for solving nonlinear global optimization problems. By using these techniques with numerical upper bound method, two case studies showed that the obtained results are reasonable and reliable while maintaining a balance between computational time and accuracy.


    Generally, in Geotechnical engineering, back analyses are used to investigate uncertain parameters. Back analyses can be done by considering known conditions, such as failure surfaces, displacements, and structural performances. In fact, solutions of many engineering problems can be formulated as the optimized results of a function. While a continuous convex optimization process governs many engineering problems, this is not the case for most Geotechnical problems. Many Geotechnical problems have irregular solution domains, with the objective function being nonconvex and may not be a continuous function. As such, a complex nonlinear optimization function is typically required for most Geotechnical problems in order to attain better understandings of these uncertainties. Therefore, Particle Swarm Optimization (PSO) and Genetic Algorithm (GA) are utilized in this thesis to facilitate in back analyses based on upper bound finite element limit analysis method. These techniques are part of evolutionary computation, which is appropriate for solving nonlinear global optimization problems. By using these techniques with numerical upper bound method, two case studies showed that the obtained results are reasonable and reliable while maintaining a balance between computational time and accuracy.

    TABLE OF CONTENT CHAPTER 1......1 INTRODUCTION....1 1.1. Motivation and Background......1 1.2. Thesis Structure....... 2 CHAPTER 2...... 3 LITERATURE REVIEW...... 3 2.1 Slope Stability Problems....... 3 2.2 Failure Modes and Failure Mechanism for Slopes...... 4 2.2.1 Rotation Shear Failure...... 4 2.2.2 Plane Shear Failure...... 6 2.2.3 Crushing, Buckling and Toppling Failure...... 7 2.3 Earthquake-Triggered Rock Slope Failures...... 9 2.4 Empirical Failure Criteria for Rock Masses...... 10 2.4.1 The Generalized Hoek-Brown Failure Criterion...... 10 2.4.2 Mohr-Coulomb Criterion...... 12 2.5 Numerical Approach...... 14 2.5.1 Limit Analysis...... 14 2.6 Back-Analysis...... 16 2.7 Optimization Methods for Slope Stability Analysis...... 19 CHAPTER 3...... 21 METHODOLOGY...... 21 3.1 Introduction...... 21 3.2 Nature Inspired Metaheuristic Algorithms...... 23 3.2.1 Genetic Algorithms...... 25 3.2.1.1 Basic Terminology...... 27 3.2.1.2 Implementation of Genetic Algorithm...... 28 3.2.1.3 Genetic Algorithm Pseudo-code...... 31 3.2.1.4 Genetic Algorithm Parameter and Operator Settings...... 32 3.2.2 Particle Swarm Optimization...... 33 3.2.2.1 Description...... 33 3.2.2.2 Characteristics of PSO...... 33 3.2.2.3 PSO Algorithm Parameters...... 37 3.2.2.4 PSO Pseudo-Code ...... 40 3.3 The Limit Theorems...... 41 3.3.1 Upper Bound Limit Analysis...... 42 CHAPTER 4 CASE STUDIES...... 45 4.1 CASE STUDY 1...... 45 4.1.1 Introduction...... 45 4.1.2 Overview of Slope Failure at a Barite Open Pit Mine...... 45 4.1.3 Numerical Model of the Rock Slope...... 48 4.1.4 The Objective Function...... 49 4.1.5 Results...... 50 4.1.5.1 Back Calculation of One Uncertainty Slope Parameter...... 50 4.1.5.2 Back Calculation of Two Uncertainty Slope Parameters...... 52 4.1.5.3 Back Calculation of Three Uncertainty Slope Parameters...... 53 4.1.5.4 Back Calculation of Four Uncertainty Slope Parameters...... 54 4.1.5.5 Comparison of Failure Mechanisms...... 55 4.1.6 Statistical Study on the Distribution and Range of D...... 57 4.2 CASE STUDY 2...... 67 4.2.1 Introduction...... 67 4.2.2 Outline of the Wenchuan earthquake and the Daguangbao landslide...... 67 4.2.3 Physical and Mechanical Properties of the Rock Slope...... 71 4.2.4 Geometry of the Upper Bound Finite Element Limit Analysis (UB-FELA) Model..... 72 4.2.5 Objective Function 73 4.2.6 Results...... 73 4.2.6.1 Back Analyzing the Horizontal Seismic Coefficient (kh) Slope Parameter...... 73 4.2.6.2 Back Analyzing kh and σci Slope Parameters...... 74 4.2.6.3 Back Analyzing kh, σci and GSI Slope Parameters...... 75 4.2.6.4 Back Analyzing kh, σci , GSI and mi Slope Parameters...... 76 4.2.6.5 Comparisons of Failure Mechanisms...... 77 4.2.7 Statistical Study...... 80 4.2.7.1 Results...... 80 CHAPTER 5...... 87 CONCLUSION AND FUTURE STUDY...... 87 5.1 Conclusion...... 87 5.2 Future Study...... 88

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