This book introduces systematically the application of Bayesian probabilistic approach in soil mechanics and geotechnical engineering. Four typical problems are analyzed by using Bayesian probabilistic approach, i.e., to model the effect of initial void ratio on the soil–water characteristic curve (SWCC) of unsaturated soil, to select the optimal model for the prediction of the creep behavior of soft soil under one-dimensional straining, to identify model parameters of soils and to select constitutive model of soils considering critical state concept. This book selects the simple and easy-to-understand Bayesian probabilistic algorithm, so that readers can master the Bayesian method to analyze and solve the problem in a short time. In addition, this book provides MATLAB codes for various algorithms and source codes for constitutive models so that readers can directly analyze and practice.
This book is useful as a postgraduate textbook for civil engineering, hydraulic engineering, transportation, railway, engineering geology and other majors in colleges and universities, and as an elective course for senior undergraduates. It is also useful as a reference for relevant professional scientific researchers and engineers.
Author(s): Wan-Huan Zhou, Zhen-Yu Yin, Ka-Veng Yuen
Publisher: Springer Singapore
Year: 2021
Language: English
Pages: 324
City: Singapore
Foreword
Preface
Acknowledgments
About This Book
Contents
About the Authors
Abbreviations
1 Problem of Uncertainties in Geotechnical Engineering
1.1 Uncertainties in Geotechnical Engineering
1.1.1 Uncertainties in Soil Properties
1.1.2 Uncertainties in Geotechnical Model
1.1.3 Back Analysis Methods
1.2 Bayesian Probabilistic Approach
1.2.1 Parametric Identification
1.2.2 Model Class Selection
1.2.3 Numerical Simulation for the Representation of the Updated PDF
1.3 Soil Water Retention Property of Unsaturated Soil
1.3.1 Soil Suction
1.3.2 Soil–Water Characteristic Curve (SWCC)
1.3.3 Influencing Factors on SWCC
1.3.4 Estimation Methods for SWCC
1.4 Creep Behavior of Soft Soil
1.4.1 Mechanism of Creep Behavior for Soft Soil
1.4.2 Time-Dependent Model for Creep Analysis
1.4.3 Bjerrum’s Time Line Conceptual Model
1.4.4 Yin and Graham’s Elastic Viscoplastic (EVP) Model
1.5 Critical State Behavior of Granular Soils
1.5.1 Shear Behavior of Granular Soils
1.5.2 Critical State Line of Granular Soils
1.6 Summary
References
2 Estimation of SWCC and Permeability for Granular Soils
2.1 Introduction
2.2 Estimation of SWCCs with Different Initial Dry Densities
2.2.1 Fredlund and Xing [7] Equation for SWCC
2.2.2 Effect of Initial Dry Density and Proposed Estimation Method
2.3 Verification and Discussion
2.4 Bayesian Approach and Confidence Interval of SWCC
2.5 Estimation of Relative Permeability Function kr
2.6 Conclusions
References
3 Modeling SWCC for Coarse-Grained and Fine-Grained Soil
3.1 Introduction
3.2 Establishment of Relationships
3.2.1 Relationship of Volumetric Water Contents
3.2.2 Relationship of Suctions
3.3 Determination of the Adjustment Parameter β
3.3.1 Established of Predictive Model
3.3.2 Model Class Selection
3.4 Results
3.4.1 Optimal Model for β
3.4.2 Comparison of SWCCs Predicted by Different Methods
3.4.3 Verification and Uncertainty Analysis
3.5 Summary
References
4 Model Updating and Uncertainty Analysis for Creep of Clay
4.1 Introduction
4.2 Review of Time-Dependent Models for Soft Soils
4.3 Model Updating with Bayesian Method
4.4 Case Study
4.4.1 Case Study 1: Intact Soft Soil Sample of Vanttila Clay
4.4.2 Case Study 2: Reconstituted Sample of Hong Kong Marine Clay
4.5 Conclusions
References
5 Effect of Loading Duration on Uncertainty in Creep Analysis for Clay
5.1 Introduction
5.2 Data for Training and Testing
5.3 Results and Discussion
5.3.1 Results by TMCMC Method
5.3.2 Estimations by Updated Models
5.3.3 Verification and Uncertainty Analysis
5.4 Conclusions
References
6 Model Class Selection for Sand with Generalization Ability Evaluation
6.1 Introduction
6.2 Representative Advanced Sand Models
6.3 Model Class Selection Approach and Generalization Ability Evaluation
6.3.1 Bayesian Model Class Selection
6.3.2 Evaluation of Generalization Ability
6.3.3 General Procedure of Model Class Selection
6.3.4 Illustration Case of Bayesian Model Class Selection
6.4 Selection of Sand Models
6.4.1 Model Class Selection Based on Different Test Sets
6.4.2 Results and Discussion
6.5 Validation by Other Sand
6.5.1 Model Class Selection Based on Tests of Karlsruhe Sand
6.5.2 Results and Discussion
6.6 Discussion
6.7 Conclusions
References
7 Parametric Identification of Advanced Soil Models for Sand
7.1 Introduction
7.2 Enhanced DE-TMCMC-Based Bayesian Identification with Parallel Computing
7.2.1 Framework of Bayesian Parameter Identification
7.2.2 Proposition of Enhanced DE-TMCMC
7.2.3 Parallel Computing DE-TMCMC
7.3 Performance of Parallel Computing DE-TMCMC
7.3.1 Numerical Validation on Synthetic Laboratory Tests
7.3.2 Performance on Real Laboratory Tests
7.3.3 Results and Discussions
7.4 Application to In Situ Testing
7.4.1 Parametric Identification Parameters from Pressuremeter Test
7.4.2 Results and Discussions
7.5 Discussions
7.6 Conclusions
References
8 Estimation of Pullout Shear Strength of Grouted Soil Nails
8.1 Introduction
8.2 Construction of Predictive Formula Candidates
8.3 Bayesian Approach
8.3.1 Bayesian Model Class Selection
8.3.2 Multivariate Linear Model
8.4 Analysis of Laboratory Test Data and Proposed Design Formula
8.4.1 Influence of Different Prior PDFs
8.4.2 Proposed Model
8.5 Comparison with the Effective Stress Method
8.6 Estimation of Field Pullout Test Data
8.7 Conclusions
References
9 Selection of Physical and Chemical Properties of Natural Fibers for Predicting Soil Reinforcement
9.1 Introduction
9.2 Basic Index of Materials
9.2.1 Chemical Components of Natural Fibers
9.2.2 Physical Properties of Natural Fibers
9.2.3 Soil Properties
9.3 Bayesian Nonparametric General Regression Method
9.4 Results and Discussion
9.4.1 Data Preparation and Model Class Selection
9.4.2 Validation of the Optimal Model
9.4.3 Robustness of the BNGR Algorithm Using the K-Fold Cross-Validation Method
9.4.4 Sensitivity Analysis of Different Influencing Factors
9.4.5 Relationships Between the UCS and the Input Variables
9.5 Conclusions
References
10 An Efficient Probabilistic Back-Analysis Method for Braced Excavations
10.1 Introduction
10.2 Method for Updating the Soil Parameters
10.2.1 Framework for Bayesian Updating
10.2.2 Procedure for the Markov Chain Monte Carlo Simulation Using the Metropolis–Hastings Algorithm
10.2.3 Response Surface Method
10.3 Measurement Errors and Model Errors
10.3.1 Model Errors of the Wall Deflections in Braced Excavations
10.3.2 Measurement Errors of Inclinometer Instruments
10.4 Illustrative Example
10.4.1 Excavation of a Metro Project in Hangzhou, China and FEM Simulation
10.4.2 Prior Knowledge of Soil Parameters
10.4.3 Construction of Response Surfaces
10.4.4 Bayesian Updating Using Prior Information from the Laboratory Test
10.4.5 Effect of Prior Distribution of Soil Parameters
10.5 Conclusions
References
Appendix A Matlab Code for Linear Model Class Selection
Appendix B Matlab Code for Soft Soil Creep Models with Finite Difference Method
Appendix C Matlab Code for Transitional Markov Chain Monte Carlo Simulation
Appendix D Matlab Code for Identifying Parameters of a Given Model or Model Class Selection for Given Experimental Data
Appendix E Matlab Code of DE-TMCMC Method
Appendix F Fortran Code of SIMSAND Model in Format of UMAT of ABAQUS
Appendix G Matlab Code for Using Bayesian Nonparametric General Regression Method
Appendix H Matlab Code for Markov Chain Monte Carlo Sampling
