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研究生： 木安利
Andri - Mulia
論文名稱： 應用多目標粒子群演算法識別土壤組成律參數之研究
Identification of Soil Constitutive Soil Model Parameters Using Multi-Objective Particle Swarming Optimization
指導教授： 謝佑明
Yo-Ming Hsieh
口試委員: 楊亦東
I-Tung Yang
歐章煜
Chang-Yu Ou
林宏達
Horn-Da Lin
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： 2012
畢業學年度： 100
語文別： 英文
論文頁數： 182
中文關鍵詞： 關鍵字 、粒子群演算法 、土壤組成律模型 、參數識別 、最佳化 、土壤試驗
外文關鍵詞： Particle Swarming Method, Soil Constitutive Model, Laboratory Test
相關次數： 點閱：126  下載：9 
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    • 本研究嘗試使用粒子群演算法 (particle swarming optimization, PSO) 根據試驗室量測資料來識別土壤組成律所需之參數。本研究考慮並實作了三個不同的組成律模式：Modified Cam-Clay (MCC)、SClay-1以及MIT-S1，這三個模式分別有4、6和16個參數需要被定義，因此其參數識別的困難性也隨之增加。而參數識別的方法乃藉由 PSO 最小化試驗室的土壤力學標準實驗之量測數據與利用土壤組成律模擬相同實驗之間的差異來達成，而此差異即利用最佳化時的目標函數加以定義。而更進一步地，本研究為同時考慮多種不同的試驗室實驗，利用多樣的試驗室量測數據配合多目標的最佳化方法來進行參數識別，以期能得更理想的土壤組成律參數。

    本研究共實現了單目標 (SO-PSO) 與多目標 (MO-PSO) 粒子群演算法,，以及三種不同的土壤組成律模式，並分別驗證了其實現的正確性，而土壤組成律模式的參數識別的正確性與準確度也在本研究中被呈現與探討。另外，本研究也探討了粒子群演算法中各個參數 (例如：世代數、粒子數) 對於土壤參數識別運算效能的影響。最後，利用台北地區粉土質粘土之試室內試驗資料，證明本研究所開發之
    軟體與程序適用於實際量測資料。

    This thesis presents a study that uses an evolutionary method called the particle swarming optimization (PSO) for identifying soil constitutive model parameters. Three constitutive models, namely Modified Cam-Clay (MCC), SClay-1, and MIT-S1, have been implemented in this study. These models have respectively 4, 6, and 16 parameters that need to be identified, giving increasing difficulty in the identification process. The identification is done using standard laboratory testing data and tries to minimize the difference between model calculations and measured data. This difference is defined by objective-function or fitness function. Furthermore, this thesis pursues the use of multi-objective optimization to deal with multiple testing data. It is a common practice to have multiple tests done on a soil in order to measure various properties of soils, and multiple tests can generate multiple evidences to help better identify parameters used in soil models. Finally, the algorithm chosen for optimization is PSO for its easiness of implementation and its effectiveness.

    Each implemented soil constitutive model, SO-PSO and MO-PSO are validated. Parameter identification of soil model parameters are presented and discussed in this thesis. In addition, a discussion regarding parameters controlling PSO, iteration count, particle number, and objective function selection is discussed in order to know their effect to PSO performance related to the parameters identification. Finally, a case-study for Taipei Silty Clay has been carried out to prove the developed procedure can be used successfully for real measured data.

    Abstract Acknowledgment Table of Contents List of Figures List of Tables CHAPTER 1 INTRODUCTION 1.1 Background 1.2 Objectives and Scopes 1.3 Methodology and Thesis Organization CHAPTER 2 LITERATURE REVIEW 2.1 Soil Constitutive Model 2.1.1 MCC Model 2.1.2 SClay-1 Model 2.1.3 MIT-S1 Model 2.1.4 Comparison of The Three Constitutive Models 2.2 Geotechnical Laboratory Tests for Determining Soil Behaviors 2.2.1 Triaxial Test 2.2.1.1 Triaxial Drained Compression Test 2.2.1.2 Triaxial Undrained Compression Test 2.2.2 Consolidation Test 2.3 Application of Optimizations in Geotechnical Engineering 2.4 Particle Swarming Optimization 2.4.1 Multi-Objective PSO (MO-PSO) CHAPTER 3 Implementation of Soil Model and Lab Test 3.1 Stress and Strain 3.1.1 Tensor() Class 3.2 SoilModel() class 3.2.1 ElastoPlastic() class 3.2.2 MitS1() class 3.3 LabTest() Class 3.3.1 TriaxialUS() Class 3.3.2 TriaxialDS() Class 3.3.3 OneD() Class 3.3.4 AnisotropicConsolidation() Class 3.4 Summary CHAPTER 4 Validations of Implemented Soil Models 4.1 Implemented MCC Validations 4.2 Implemented SClays-1 Validations 4.3 Implemented MIT-S1 Validations 4.4 Summary CHAPTER 5 Multi-Objective Particle Swarming Optimization (MO-PSO) 5.1 Implementation of Single-Objective PSO (SO-PSO) 5.2 Implementation of MO-PSO 5.3 Validation of PSO 5.3.1 Validation of Implemented SO-PSO 5.3.2 Validation of Implemented MO-PSO 5.4 Summary CHAPTER 6 Identification of Soil Model Parameters 6.1 Sensitivity of Soil Models Parameters 6.1.1 MCC parameters 6.1.2 SClay-1 Parameters 6.1.3 MIT-S1 Parameters 6.2 Objective Function, Decision Variable, and Constraint for Soil Model Parameters Identification 6.3 Validation for MCC Parameters Identification 6.4 Validation for SClay-1 Parameters Identification 6.5 Validation for MIT-S1 parameters Identification 6.6 State Parameter Identification 6.7 Summary CHAPTER 7 DISCUSSION 7.1 Selecting Objective Function 7.2 Effect of the Parameters Controlling PSO 7.2.1 C1 and C2 7.2.2 Vmax 7.2.3 Inertia Weight (W) 7.3 Effect Particles Number and Iterations count 7.4 SO-PSO Versus MO-PSO in Predicting Soil Models Parameters 7.5 Summary CHAPTER 8 A CASE STUDY ON TAIPEI SILTY CLAY 8.1 Predicted MCC Parameters 8.1.1 Single-Objective 8.1.2 Multi-Objective 8.2 Predicted SClay-1 Parameters 8.2.1 Single-Objective 8.2.2 Multi-Objective 8.3 Predicted MIT-S1 Parameters 8.3.1 Single Objective 8.3.2 Multi-Objective 8.4 Comparison Predicted Parameters 8.4.1 Comparison Predicted Parameters 8.4.2 Comparison by Applying Identified Parameter to Simulate OC CIUC 8.5 Summary CHAPTER 9 SUMMARY AND RECOMMENDATION 9.1 Summary 9.2 Recommendation Notations Reference Appendix A

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