Machine Learning in Geohazard Risk Prediction and Assessment: From Microscale Analysis to Regional Mapping presents an overview of the most recent developments in machine learning techniques that have reshaped our understanding of geo-materials and management protocols of geo-risk. The book covers a broad category of research on machine-learning techniques that can be applied, from microscopic modeling to constitutive modeling, to physics-based numerical modeling, to regional susceptibility mapping. This is a good reference for researchers, academicians, graduate and undergraduate students, professionals, and practitioners in the field of geotechnical engineering and applied geology.
1. Chapter 5. Introduction to system reliability analysis for geotechnical infrastructures - p. 109 2. Chapter 6. Deep learning for surrogate modeling for geotechnical risk analysis - p. 143 3. Chapter 7. Physics-based deep-learning numerical methods: application to groundwater seepage and consolidation problems - p. 183 4. Part 4: Machine learning in data-drivin geotechnical engineering - p. P215 5. Chapter 8. Deep learning for time series forecasting in tunneling - p. 217 6. Chapter 9. Computational intelligence in rock engineering management systems - p. 233 7. Chapter 10. Genetic programming for the prediction of tunnel face support pressure - p. 287 8. Chapter 11. Machine learning in geo-risk susceptibility mapping - p. 299 9. Chapter 12. Landslide modeling in the age of AI: A review of physically based and machine learning approaches and their potential integration - p. 335 10. Index - p. 355
Machine Learning in Geohazard Risk Prediction and Assessment: From Microscale Analysis to Regional Mapping presents an overview of the most recent developments in machine learning techniques that have reshaped our understanding of geo-materials and management protocols of geo-risk. The book covers a broad category of research on machine-learning techniques that can be applied, from microscopic modeling to constitutive modeling, to physics-based numerical modeling, to regional susceptibility mapping. This is a good reference for researchers, academicians, graduate and undergraduate students, professionals, and practitioners in the field of geotechnical engineering and applied geology.
1. Chapter 5. Introduction to system reliability analysis for geotechnical infrastructures - p. 109 2. Chapter 6. Deep learning for surrogate modeling for geotechnical risk analysis - p. 143 3. Chapter 7. Physics-based deep-learning numerical methods: application to groundwater seepage and consolidation problems - p. 183 4. Part 4: Machine learning in data-drivin geotechnical engineering - p. P215 5. Chapter 8. Deep learning for time series forecasting in tunneling - p. 217 6. Chapter 9. Computational intelligence in rock engineering management systems - p. 233 7. Chapter 10. Genetic programming for the prediction of tunnel face support pressure - p. 287 8. Chapter 11. Machine learning in geo-risk susceptibility mapping - p. 299 9. Chapter 12. Landslide modeling in the age of AI: A review of physically based and machine learning approaches and their potential integration - p. 335 10. Index - p. 355
