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Introduction and Applications of Machine Learning in Geotechnics

Artikelnummer: 978-0-443-41481-7
Einband: Kartonierter Einband (Kt)
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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

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

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VerlagElsevier
EinbandKartonierter Einband (Kt)
Erscheinungsjahr2026
Seitenangabe388 S.
AusgabekennzeichenEnglisch
MasseH22.9 cm x B15.2 cm 620 g
CoverlagElsevier - Health Sciences Division (Imprint/Brand)
AutorGeem, Zong Woo / Bekdas, Gebrail / Nigdeli, Sinan Melih / Aydin, Yaren / Isikdag, Ümit / Kim, Tae-Hyung

Über den Autor 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.

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