Introduction and Applications of Machine Learning in Geotechnics
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60 148 Ft
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Product details:
- Publisher Elsevier Science
- Date of Publication 20 April 2026
- ISBN 9780443414817
- Binding Paperback
- No. of pages388 pages
- Size 229x152 mm
- Weight 450 g
- Language English 697
Categories
Long description:
Introduction and Applications of Machine Learning in Geotechnics offers a comprehensive exploration of machine learning methodologies and their diverse applications in geotechnical engineering. The book begins with a detailed review of machine learning methods tailored for geotechnical applications, setting the foundation for subsequent chapters. Regression models are utilized to predict shear wave velocities while optimization-based approaches are employed to determine the optimal dimensions of reinforced concrete (RC) retaining walls. The book further explores the identification of gravelly soil through optimized machine learning models and predicts stress-strain responses using data from simple shear tests.
Additionally, it outlines the forecasting of liquefaction events triggered by seismic activities and estimates the uniaxial compressive strength of soil using machine learning techniques. The prediction of vertical effective stress and specific penetration resistance is examined to enhance soil characterization and geotechnical analyses. The authors' provide valuable insights for geotechnical engineers and researchers seeking to leverage advanced computational tools for enhanced geotechnical assessments and design processes.
Table of Contents:
1. A review of machine learning (ML) methods for geotechnical engineering
2. Explainable artificial intelligence (XAI) in geotechnical engineering
3. Regression models for shear wave velocity prediction
4. Optimization-based approaches to predict the optimal dimensions of reinforced concrete (RC) retaining walls
5. Gravelly soil identification using optimized machine learning models
6. Prediction of stress-strain responses from simple shear tests
7. Predicting liquefaction potential from seismic events
8. Estimation of uniaxial compressive strength of soil
9. Prediction of specific penetration resistance of clayey soils
10. Slope stability prediction using machine learning models
11. Deep learning-based object detection approaches for landslide identification
12. Machine learning approaches for predicting soil swelling behavior
