Jianye Ching – författare

This book aims to introduce the principle and design of various foundations, covering shallow foundations, mat foundations, earth retaining structures, excavations, pile foundations, and slope stability. Since the analysis and design of a foundation are based on the soil properties under short-term (undrained) or long-term (drained) conditions, the assessment of soil properties from the geotechnical site investigation and the concept of drained or undrained soil properties are discussed in the first two chapters. Foundation elements transfer various load combinations from the superstructure to the underlying soils or rocks. The load transfer mechanisms, vertical stress or earth pressure distributions, and failure modes of each foundation type are clearly explained in this book. After understanding the soil responses subjected to the loadings from the foundation, the design methods, required factors of safety, and improvement measures for each foundation type are elaborated.

This book presents both theoretical explication and practical applications for readers to easily comprehend the theoretical background, design methods, and practical applications and considerations. Each chapter provides relevant exercise examples and a problem set for self-practice. The analysis methods introduced in the book can be applied in actual analysis and design as they contain the most up-to-date knowledge of foundation design. This book is suitable for teachers and students to use in foundation engineering courses and engineers who are engaged in foundation design to create a technically sound, construction-feasible, and economical design of the foundation system.

This book aims to introduce the principle and design of various foundations, covering shallow foundations, mat foundations, earth retaining structures, excavations, pile foundations, and slope stability. Since the analysis and design of a foundation are based on the soil properties under short-term (undrained) or long-term (drained) conditions, the assessment of soil properties from the geotechnical site investigation and the concept of drained or undrained soil properties are discussed in the first two chapters. Foundation elements transfer various load combinations from the superstructure to the underlying soils or rocks. The load transfer mechanisms, vertical stress or earth pressure distributions, and failure modes of each foundation type are clearly explained in this book. After understanding the soil responses subjected to the loadings from the foundation, the design methods, required factors of safety, and improvement measures for each foundation type are elaborated.

This book presents both theoretical explication and practical applications for readers to easily comprehend the theoretical background, design methods, and practical applications and considerations. Each chapter provides relevant exercise examples and a problem set for self-practice. The analysis methods introduced in the book can be applied in actual analysis and design as they contain the most up-to-date knowledge of foundation design. This book is suitable for teachers and students to use in foundation engineering courses and engineers who are engaged in foundation design to create a technically sound, construction-feasible, and economical design of the foundation system.

Uncertainty, Modeling, and Decision Making in Geotechnics shows how uncertainty quantification and numerical modeling can complement each other to enhance decision-making in geotechnical practice, filling a critical gap in guiding practitioners to address uncertainties directly.

The book helps practitioners acquire a working knowledge of geotechnical risk and reliability methods and guides them to use these methods wisely in conjunction with data and numerical modeling. In particular, it provides guidance on the selection of realistic statistics and a cost-effective, accessible method to address different design objectives, and for different problem settings, and illustrates the value of this to decision-making using realistic examples.

Bringing together statistical characterization, reliability analysis, reliability-based design, probabilistic inverse analysis, and physical insights drawn from case studies, this reference guide from an international team of experts offers an excellent resource for state-of-the-practice uncertainty-informed geotechnical design for specialist practitioners and the research community.

Uncertainty, Modeling, and Decision Making in Geotechnics shows how uncertainty quantification and numerical modeling can complement each other to enhance decision-making in geotechnical practice, filling a critical gap in guiding practitioners to address uncertainties directly.

The book helps practitioners acquire a working knowledge of geotechnical risk and reliability methods and guides them to use these methods wisely in conjunction with data and numerical modeling. In particular, it provides guidance on the selection of realistic statistics and a cost-effective, accessible method to address different design objectives, and for different problem settings, and illustrates the value of this to decision-making using realistic examples.

Bringing together statistical characterization, reliability analysis, reliability-based design, probabilistic inverse analysis, and physical insights drawn from case studies, this reference guide from an international team of experts offers an excellent resource for state-of-the-practice uncertainty-informed geotechnical design for specialist practitioners and the research community.

Bayesian data analysis and modelling linked with machine learning offers a new tool for handling geotechnical data. This book presents recent advancements made by the author in the area of probabilistic geotechnical site characterization.

Two types of correlation play central roles in geotechnical site characterization: cross-correlation among soil properties and spatial-correlation in the underground space. The book starts with the introduction of Bayesian notion of probability “degree of belief”, showing that well-known probability axioms can be obtained by Boolean logic and the definition of plausibility function without the use of the notion “relative frequency”. It then reviews probability theories and useful probability models for cross-correlation and spatial correlation. Methods for Bayesian parameter estimation and prediction are also presented, and the use of these methods demonstrated with geotechnical site characterization examples.

Bayesian Machine Learning in Geotechnical Site Characterization suits consulting engineers and graduate students in the area.

Bayesian data analysis and modelling linked with machine learning offers a new tool for handling geotechnical data. This book presents recent advancements made by the author in the area of probabilistic geotechnical site characterization.

Two types of correlation play central roles in geotechnical site characterization: cross-correlation among soil properties and spatial-correlation in the underground space. The book starts with the introduction of Bayesian notion of probability “degree of belief”, showing that well-known probability axioms can be obtained by Boolean logic and the definition of plausibility function without the use of the notion “relative frequency”. It then reviews probability theories and useful probability models for cross-correlation and spatial correlation. Methods for Bayesian parameter estimation and prediction are also presented, and the use of these methods demonstrated with geotechnical site characterization examples.

Bayesian Machine Learning in Geotechnical Site Characterization suits consulting engineers and graduate students in the area.