Advances in Computational Geomechanics
Advanced Computational Techniques and Methodologies in Geotechnical Engineering
Series: Earth Observation;
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Product details:
- Publisher Elsevier Science
- Date of Publication 1 April 2026
- ISBN 9780443277085
- Binding Paperback
- No. of pages500 pages
- Size 229x152 mm
- Language English 700
Categories
Long description:
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.
Table of Contents:
Part I: Numerical Modelling and Analysis
1. Finite Element Method (FEM) and Finite Difference Method (FDM)
2. Discrete Element Method (DEM) and DEM-FEM Coupling
3. Emerging Computational Techniques
4. Hybrid Numerical Methods and Future Directions
Part II: Stochastic, Probabilistic and Reliability Analyses
5. Stochastic Methods in Geotechnical Engineering
6. Probabilistic Approaches to Soil Characterization
7. Reliability Analysis in Geotechnical Design
8. Risk Assessment and Management in Geotechnical Projects
Part III: Artificial Intelligence and Machine Learning
9. Machine Learning Algorithms for Geotechnical Data Analysis
10. AI-Based Predictive Models for Soil Behavior and Properties
11. AI in Geotechnical Risk and Decision-Making
12. Data-Driven Approaches for Soil Classification and Site Characterization
