Advances in Computational Geomechanics: Advanced Computational Techniques and Methodologies in Geotechnical Engineering provides a comprehensive overview of cutting-edge computational methodologies in geotechnical engineering. The first part examines stochastic, probabilistic, and reliability analyses in geotechnical engineering, covering stochastic methods, probabilistic approaches to soil characterization, reliability analysis in geotechnical design, and risk assessment and management in geotechnical projects. The second part delves into artificial intelligence (AI) and machine learning applications in geotechnical engineering, including machine learning algorithms for geotechnical data analysis, AI-based predictive models for soil behavior and properties, AI in geotechnical risk and decision-making, and data-driven approaches for soil classification and site characterization.
The third part focuses on numerical modeling and analysis techniques, such as the Finite Element Method (FEM), Finite Difference Method (FDM), Discrete Element Method (DEM), and explores hybrid numerical methods and future directions in computational geomechanics. This book serves as a valuable resource for geotechnical engineers, researchers, and practitioners seeking to leverage advanced computational tools for geomechanical analyses and design.
1. Title of Book - p. i 2. Cover image - p. i 3. Title page - p. ii 4. Table of Contents - p. iii 5. Copyright - p. iv 6. List of contributors - p. ix 7. About the editors - p. xi 8. Preface - p. xiii 9. Part I: Numerical modelling and simulation - p. P1 10. Chapter one. Traditional numerical methods - p. 3 11. Chapter two. Hybrid numerical methods - p. 57 12. Chapter three. Advanced soil constitutive modeling - p. 103 13. Part II: Stochastic, probabilistic and reliability - p. P151 14. Chapter four. Probabilistic approaches to soil characterization - p. 153 15. Chapter Five. Reliability and risk assessment in geotechnical design practice with case studies - p. 175 16. Part III: Artificial intelligence and machine learning - p. P243 17. Chapter six. Machine learning algorithms for geotechnical analysis - p. 245 18. Chapter seven. Predictive models for geotechnical material behavior using intelligent computing - p. 285 19. Chapter eight. Artificial intelligence-enhanced geotechnical risk assessment and decision-making - p. 353 20. Chapter nine. Data-driven approaches for site characterization - p. 425 21. Index - p. 477
Advances in Computational Geomechanics: Advanced Computational Techniques and Methodologies in Geotechnical Engineering provides a comprehensive overview of cutting-edge computational methodologies in geotechnical engineering. The first part examines stochastic, probabilistic, and reliability analyses in geotechnical engineering, covering stochastic methods, probabilistic approaches to soil characterization, reliability analysis in geotechnical design, and risk assessment and management in geotechnical projects. The second part delves into artificial intelligence (AI) and machine learning applications in geotechnical engineering, including machine learning algorithms for geotechnical data analysis, AI-based predictive models for soil behavior and properties, AI in geotechnical risk and decision-making, and data-driven approaches for soil classification and site characterization.
The third part focuses on numerical modeling and analysis techniques, such as the Finite Element Method (FEM), Finite Difference Method (FDM), Discrete Element Method (DEM), and explores hybrid numerical methods and future directions in computational geomechanics. This book serves as a valuable resource for geotechnical engineers, researchers, and practitioners seeking to leverage advanced computational tools for geomechanical analyses and design.
1. Title of Book - p. i 2. Cover image - p. i 3. Title page - p. ii 4. Table of Contents - p. iii 5. Copyright - p. iv 6. List of contributors - p. ix 7. About the editors - p. xi 8. Preface - p. xiii 9. Part I: Numerical modelling and simulation - p. P1 10. Chapter one. Traditional numerical methods - p. 3 11. Chapter two. Hybrid numerical methods - p. 57 12. Chapter three. Advanced soil constitutive modeling - p. 103 13. Part II: Stochastic, probabilistic and reliability - p. P151 14. Chapter four. Probabilistic approaches to soil characterization - p. 153 15. Chapter Five. Reliability and risk assessment in geotechnical design practice with case studies - p. 175 16. Part III: Artificial intelligence and machine learning - p. P243 17. Chapter six. Machine learning algorithms for geotechnical analysis - p. 245 18. Chapter seven. Predictive models for geotechnical material behavior using intelligent computing - p. 285 19. Chapter eight. Artificial intelligence-enhanced geotechnical risk assessment and decision-making - p. 353 20. Chapter nine. Data-driven approaches for site characterization - p. 425 21. Index - p. 477
