Zong Woo Geem, Bekda &, Sinan Melih Nigdeli, Ayd &, I &, Tae-Hyung Kim

Introduction and Applications of Machine Learning in Geotechnics

• Broschiertes Buch

Produktbeschreibung

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.

Produktdetails

• Verlag: Elsevier Science
• Seitenzahl: 300
• Erscheinungstermin: 1. Juni 2026
• Englisch
• Gewicht: 449g
• ISBN-13: 9780443414817
• ISBN-10: 0443414815
• Artikelnr.: 75810647

Autorenporträt

Prof. Zong Woo Geem works for the Department of Smart City at Gachon University in South Korea. He has obtained B.Eng. from Chung-Ang University, M.Eng. and Ph.D. from Korea University, and M.Sc. from Johns Hopkins University, and researched at Virginia Tech, University of Maryland - College Park, and Johns Hopkins University. He invented a music-inspired optimization algorithm, Harmony Search (HS), which has been applied to various scientific and engineering problems as well as social and cultural problems. His research interest includes theoretical background (e.g. novel human-experience-based derivative of HS rather than existing analytic-calculus-based derivative) and problem-specific development (e.g. problem-specific operator of HS) of phenomenon-mimicking algorithm and its applications to sustainability & culture issues.

Inhaltsangabe

1. A review of machine learning (ML) methods for geotechnical engineering
2. Regression models for shear wave velocity prediction
3. Optimization based approaches to predict optimal dimensions of RC
retaining walls
4. Gravelly soil identification based on the optimized ML models
5. Prediction of the Stress-strain responses by simple shear tests
6. Predicting the liquefaction from seismic events
7. Estimation of uniaxial compressive strength of soil
8. Prediction of vertical effective stress (¿'v) and specific penetration
resistance (ps)
9. Slope stability prediction using ML models