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Machine Learning and Data Science in Geotechnics Cover Image for Volume 1, Issue 1
Current Issue
Volume 1,
Issue 1,
15 December 2025
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Volume 2, Issue 1, December 2026

About the Journal

Machine Learning and Data Science in Geotechnics aims to disseminate original contributions in the emerging fields of machine learning, artificial intelligence, big data analysis, and statistical approaches, with a focus on addressing various geotechnical engineering challenges.

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State-of-the-art review on predicting stabilized soil properties using artificial neural networks
Effects of sample preparation on triaxial compression tests formed with uniform spherical particles
Active learning-driven ensemble framework for efficient and robust reliability analysis in geotechnical engineering
A meta model for time-variant reliability analysis of unsaturated slope under rainfall
Optimising sample size for normality testing in geotechnical data using power analysis
Open access articles
March 24 2026
State-of-the-art review on predicting stabilized soil properties using artificial neural networks
Aparna R.P.
March 03 2026
Effects of sample preparation on triaxial compression tests formed with uniform spherical particles
Tam N.-M. Duong, Billy Hernawan et al.
February 20 2026
Active learning-driven ensemble framework for efficient and robust reliability analysis in geotechnical engineering
Xian Liu, Bin Liu et al.
February 02 2026
A meta model for time-variant reliability analysis of unsaturated slope under rainfall
Abdullah Abdullah, Mahdi Shadabfar et al.
January 06 2026
Optimising sample size for normality testing in geotechnical data using power analysis
K.S. Sankar Ganesh, T. Ashok Kumar et al.

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Editor(s):
Dr Mohammad Rezania

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