Risk Assessment and Hazard Prediction of Seismic Liquefactio
發(fā)布時(shí)間：2024-07-05 19:45
飽和砂土液化是最為典型的地震災(zāi)害之一。因此,準(zhǔn)確地對(duì)液化危害進(jìn)行評(píng)估是巖土工程領(lǐng)域的重要任務(wù)之一。地震液化評(píng)估中,需要對(duì)三個(gè)主要的方面進(jìn)行考慮:1)液化敏感性;2)觸發(fā)液化的動(dòng)態(tài)載荷的評(píng)價(jià);3)液化的影響。前兩個(gè)在分析描述液化能否發(fā)生的液化勢(shì)方面已經(jīng)得到了檢驗(yàn)。此外,在評(píng)估液化破壞效應(yīng)時(shí),主要是指橫向位移,這是進(jìn)行液化危害評(píng)估的最后一個(gè)方面。在所有的方法中,基于歷史數(shù)據(jù)或室內(nèi)試驗(yàn)結(jié)果的經(jīng)驗(yàn)和半經(jīng)驗(yàn)?zāi)Ｐ褪亲顬槠毡榍乙子诠こ處熀脱芯咳藛T所使用的一種分析手段。現(xiàn)階段,智能算法已被運(yùn)用于檢測(cè)參數(shù)與危險(xiǎn)因素之間的相關(guān)性。然而,這種方法依舊缺乏防止模型被過(guò)度訓(xùn)練的驗(yàn)證階段。雖然細(xì)粒含量(FC)對(duì)孔隙水壓力的產(chǎn)生具有復(fù)雜影響這一結(jié)論已經(jīng)得到了驗(yàn)證,但是在現(xiàn)有模型中并沒(méi)有考慮該影響參數(shù)。所有的模型都包含了此參數(shù),但對(duì)其取值并沒(méi)有加以任何限制。此外,已經(jīng)證明,標(biāo)準(zhǔn)化累積絕對(duì)速度(CAV5)為孔壓的增長(zhǎng)和液化的發(fā)生提供了最充分和有效的推動(dòng)作用。然而,在現(xiàn)有模型中,動(dòng)量大小(Mw)和水平峰值地面加速度(PGA)通常與液化危害評(píng)估相關(guān)。此外,由于天然易變性以及對(duì)土體性質(zhì)缺乏了解,幾何條件、地震荷載,巖土工程問(wèn)...
【文章頁(yè)數(shù)】：176 頁(yè)
【學(xué)位級(jí)別】：博士
【文章目錄】：
摘要
Abstract
1 Introduction
1.1 Approaches for the assessment of potential of liquefaction triggering
1.1.1 Stress-based approaches
1.1.2 Strain-based approaches
1.1.3 energy-based approaches
1.1.3.1 Approaches based on earthquake case histories
1.1.3.2 Approaches based on the Arias Intensity
1.1.3.3 Approaches based on laboratory test results
1.2 Approaches for prediction of lateral displacement due to the liquefaction
1.2.1 Review of Empirical and Semi-Empirical Models to Predict Lateral Displacement Dueto Liquefaction
1.2.1.1 Newmark sliding block analysis
1.2.1.2 Nonlinear analyses
1.3 Cumulative Absolute Velocity
1.3.1 Unpublished results
1.4 Fine Content Critical Value
1.5 Artificial Neural Network
1.6 Response Surface Methodology
1.6.1 Select of regression model function
1.6.2 Design of experiment
1.6.2.1 The concepts used in the design of experiments
1.6.2.1.1 Variables
1.6.2.1.2 Factor
1.6.2.1.3 Levels
1.6.2.1.4 Response
1.6.2.1.5 Effect
1.6.2.1.6 Interaction
1.6.2.1.7 Optimization process
1.6.3 Experimental design
1.6.3.1 Central composite design
1.6.3.1.1 Types of central composite design
1.6.3.1.1.1 Circumscribed designs
1.6.3.1.1.2 Face centred
1.6.3.2 Box- Behnken design (BBD)
1.6.3.3 Doehlert design
1.6.4 RSM advantages
1.6.5 RSM disadvantages
1.6.6 Coding of the input variables
1.6.7 Hypothesis Test
1.7 Monte Carlo simulation and uncertainties
1.8 Monte Carlo simulation using artificial neural network for parametric sensitivity analysisproposed in this study
1.9 Organization of the Thesis
2.A New Equation to Evaluate Liquefaction Triggering Using the Response Surface Methodand Parametric Sensitivity Analysis
2.1 Introduction
2.2 Case History Database
2.2.1 Earthquake magnitude and peak accelerations
2.2.2 The selection and measurement of qc1 Ncs values
2.2.3 Moss landing state beach
2.2.4 Wildlife liquefaction array
2.2.5 Miller and Farris farms
2.2.6 Malden Street
2.2.7 The classification of site performance
2.3 Proposed Model and Equation to Evaluate Liquefaction Triggering
2.4. Results
2.4.1 RSM Equation to Evaluate Liquefaction Triggering
2.4.2 Sensitivity Analysis with the Monte Carlo Simulation Method
2.5 Summary and Conclusions
3. Energy Evaluation of Triggering Soil Liquefaction Based on the Response Surface Methodand Parametric Sensitivity Analyse
3.1 Introduction
3.2 The mechanisms of energy dissipation in sand
3.2.1 Hysteresis loops
3.2.2 Equal linearization and damping ratios
3.2.3 The use of dissipated energy to quantify capacity
3.3 Databases and artificial neural network models
3.3.1 First artificial neural network mode
3.3.2 Second artificial neural network mode
3.4 The RSM Equations
3.5 Comparison of the predicted capacity energy liquefaction by the RSM equations andexisting model
3.6 Comparison of the predicted capacity energy liquefaction by the ANN models andexisting models
3.7 Sensitivity analysis
3.8 Summary and Conclusion
4. New Equations to Evaluate Lateral Displacement Caused by Liquefaction Using theResponse Surface Method and Parametric Sensitivity Analysis
4.1 Introduction
4.2 Patterns of Lateral displacement deformation
4.3 Models for lateral displacement measurement
4.4 Dataset
4.5 Artificial Neural Network Models
4.5.1 Comparison of ANN models with Extra Model
4.6 The RSM Equations for Predicting DH
4.6.1 Comparison of RSM Equations with Extra Models
4.7 Sensitivity Analysis
4.8 Results and Discussion
4.9 Summary and Conclusions
5. Conclusions and Prospects
5.1 Conclusions
5.2 Innovation Points
5.3 Outlook
References
Appendix A
Appendix B
Appendix C
Appendix D
Appendix E
Research Projects and Publications During PhD Period
Acknowledgement
Curriculum Vitae
本文編號(hào)：4001437
【文章頁(yè)數(shù)】：176 頁(yè)
【學(xué)位級(jí)別】：博士
【文章目錄】：
摘要
Abstract
1 Introduction
1.1 Approaches for the assessment of potential of liquefaction triggering
1.1.1 Stress-based approaches
1.1.2 Strain-based approaches
1.1.3 energy-based approaches
1.1.3.1 Approaches based on earthquake case histories
1.1.3.2 Approaches based on the Arias Intensity
1.1.3.3 Approaches based on laboratory test results
1.2 Approaches for prediction of lateral displacement due to the liquefaction
1.2.1 Review of Empirical and Semi-Empirical Models to Predict Lateral Displacement Dueto Liquefaction
1.2.1.1 Newmark sliding block analysis
1.2.1.2 Nonlinear analyses
1.3 Cumulative Absolute Velocity
1.3.1 Unpublished results
1.4 Fine Content Critical Value
1.5 Artificial Neural Network
1.6 Response Surface Methodology
1.6.1 Select of regression model function
1.6.2 Design of experiment
1.6.2.1 The concepts used in the design of experiments
1.6.2.1.1 Variables
1.6.2.1.2 Factor
1.6.2.1.3 Levels
1.6.2.1.4 Response
1.6.2.1.5 Effect
1.6.2.1.6 Interaction
1.6.2.1.7 Optimization process
1.6.3 Experimental design
1.6.3.1 Central composite design
1.6.3.1.1 Types of central composite design
1.6.3.1.1.1 Circumscribed designs
1.6.3.1.1.2 Face centred
1.6.3.2 Box- Behnken design (BBD)
1.6.3.3 Doehlert design
1.6.4 RSM advantages
1.6.5 RSM disadvantages
1.6.6 Coding of the input variables
1.6.7 Hypothesis Test
1.7 Monte Carlo simulation and uncertainties
1.8 Monte Carlo simulation using artificial neural network for parametric sensitivity analysisproposed in this study
1.9 Organization of the Thesis
2.A New Equation to Evaluate Liquefaction Triggering Using the Response Surface Methodand Parametric Sensitivity Analysis
2.1 Introduction
2.2 Case History Database
2.2.1 Earthquake magnitude and peak accelerations
2.2.2 The selection and measurement of qc1 Ncs values
2.2.3 Moss landing state beach
2.2.4 Wildlife liquefaction array
2.2.5 Miller and Farris farms
2.2.6 Malden Street
2.2.7 The classification of site performance
2.3 Proposed Model and Equation to Evaluate Liquefaction Triggering
2.4. Results
2.4.1 RSM Equation to Evaluate Liquefaction Triggering
2.4.2 Sensitivity Analysis with the Monte Carlo Simulation Method
2.5 Summary and Conclusions
3. Energy Evaluation of Triggering Soil Liquefaction Based on the Response Surface Methodand Parametric Sensitivity Analyse
3.1 Introduction
3.2 The mechanisms of energy dissipation in sand
3.2.1 Hysteresis loops
3.2.2 Equal linearization and damping ratios
3.2.3 The use of dissipated energy to quantify capacity
3.3 Databases and artificial neural network models
3.3.1 First artificial neural network mode
3.3.2 Second artificial neural network mode
3.4 The RSM Equations
3.5 Comparison of the predicted capacity energy liquefaction by the RSM equations andexisting model
3.6 Comparison of the predicted capacity energy liquefaction by the ANN models andexisting models
3.7 Sensitivity analysis
3.8 Summary and Conclusion
4. New Equations to Evaluate Lateral Displacement Caused by Liquefaction Using theResponse Surface Method and Parametric Sensitivity Analysis
4.1 Introduction
4.2 Patterns of Lateral displacement deformation
4.3 Models for lateral displacement measurement
4.4 Dataset
4.5 Artificial Neural Network Models
4.5.1 Comparison of ANN models with Extra Model
4.6 The RSM Equations for Predicting DH
4.6.1 Comparison of RSM Equations with Extra Models
4.7 Sensitivity Analysis
4.8 Results and Discussion
4.9 Summary and Conclusions
5. Conclusions and Prospects
5.1 Conclusions
5.2 Innovation Points
5.3 Outlook
References
Appendix A
Appendix B
Appendix C
Appendix D
Appendix E
Research Projects and Publications During PhD Period
Acknowledgement
Curriculum Vitae
本文編號(hào)：4001437
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