Introduction and Applications of Machine Learning in Geotechnics
- 1st Edition - April 20, 2026
- Latest edition
- Authors: Zong Woo Geem, Gebrail Bekdaş, Sinan Melih Nigdeli, Yaren Aydın, Ümit Işıkdağ, Tae-Hyung Kim
- Language: English
Introduction and Applications of Machine Learning in Geotechnics offers a comprehensive exploration of machine learning methodologies and their diverse applications in geotec… Read more
Description
Description
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.
Key features
Key features
- 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
Readership
Readership
Table of contents
Table of contents
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
Product details
Product details
- Edition: 1
- Latest edition
- Published: April 20, 2026
- Language: English
About the authors
About the authors
ZG
Zong Woo Geem
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.
GB
Gebrail Bekdaş
Gebrail Bekdaş, Professor, is researcher in Mechanics at Istanbul University - Cerrahpaşa. 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.
SN
Sinan Melih Nigdeli
Sinan Melih Nigdeli, Professor, is researcher in Mechanics at Istanbul University - Cerrahpaşa. 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.
YA
Yaren Aydın
Yaren Aydın is research assistant in Mechanics at Istanbul University - Cerrahpaşa. Yaren Aydın is studying at İstanbul University-Cerrahpaşa, 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.
ÜI
Ümit Işıkdağ
Ümit Işıkdağ 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).
TK