Introduction and Applications of Machine Learning in Geotechnics
Published on 20. April 2026
300 pages
978-0-443-41482-4 (ISBN)
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 book's authors provide valuable insights for geotechnical engineers and researchers seeking to leverage advanced computational tools for enhanced geotechnical assessments and design processes.
- Provides a systematic overview of machine learning and optimization applications in geotechnical engineering
- Demonstrates real-world implementations for soil classification, seismic response, and structural-geotechnical interaction
- Introduces interpretable and explainable AI methods for transparent engineering decision-making
- Emphasizes sustainable and data-driven solutions through hybrid modeling and metaheuristic optimization
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Zong Woo Geem | Gebrail Bekdas | Sinan Melih Nigdeli
Introduction and Applications of Machine Learning in Geotechnics
Book
04/2026
Elsevier
€152.50
Shipment within 15-20 days
Persons
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.Gebrail Bekdas, Professor, is researcher in Mechanics at Istanbul University - Cerrahpasa. He obtained his DPhil in Structural Engineering from Istanbul University with a thesis subject of design of cylindrical walls. He was one of the guest editors in 2017 special issue of KSCE Journal of Civil Engineering. He co-chaired 6th International Conference on Harmony Search, Soft Computing and Applications (ICHSA 2020). He has authored about 500 papers for journals and scientific events.Sinan Melih Nigdeli, Professor, is researcher in Mechanics at Istanbul University - Cerrahpasa. He obtained his DPhil in Structural Engineering from Istanbul Technical University with a thesis subject of active control. He was one of the guest editors in 2017 special issue of KSCE Journal of Civil Engineering. He co-chaired 6th International Conference on Harmony Search, Soft Computing and Applications (ICHSA 2020). He has authored about 500 papers for journals and scientific events.Yaren Aydin is research assistant in Mechanics at Istanbul University - Cerrahpasa. Yaren Aydin is studying at Istanbul University-Cerrahpasa, Institute of Postgraduate Education, Department of Civil Engineering, PhD program on the subject of Artificial Intelligence-Machine Learning. In her master thesis, Ms. Yaren AYDIN applied important technical knowledge: computer programming, optimization and analysis of various types of structures. In today's world, most engineering problems are based on maximum efficiency with limited resources, leading to optimization processes that involve civil engineering knowledge and computer programming. She has also carried out various studies on artificial intelligence and machine learning in her articles and papers. There are also studies that combine optimization and artificial intelligence. She has participated in many conferences on optimization and machine learning.Ümit Isikdag is currently working as a Professor of Construction Informatics at the Department of Architecture in Mimar Sinan Fine Arts University. He has a PhD in Construction Informatics from the University of Salford, U.K.His current research interests are in Machine and Deep Learning, Internet of Things, 3D GIS, BIM and Structural Equation Modeling. He has started teaching in 1999 and since then he has worked full and part time at (chronological order:) Ege University, Beykent University, Istanbul Kultur University, Istanbul University, Istanbul Esenyurt University, Bogazici University, Istanbul Technical University. In addition, he has worked as a senior researcher at Coventry University (2004), and the University of Central Lancashire (2013).
Content
1. A review of machine learning (ML) methods for geotechnical engineering2. Explainable artificial intelligence (XAI) in geotechnical engineering3. Regression models for shear wave velocity prediction4. Optimization-based approaches to predict the optimal dimensions of reinforced concrete (RC) retaining walls5. Gravelly soil identification using optimized machine learning models6. Prediction of stress-strain responses from simple shear tests7. Predicting liquefaction potential from seismic events8. Estimation of uniaxial compressive strength of soil9. Prediction of specific penetration resistance of clayey soils10. Slope stability prediction using machine learning models11. Deep learning-based object detection approaches for landslide identification12. Machine learning approaches for predicting soil swelling behavior
