The Application of Machine Learning Techniques in Geotechnical Engineering: A Review and Comparison

W Shao, W Yue, Y Zhang, T Zhou, Y Zhang, Y Dang… - Mathematics, 2023 - mdpi.com
With the development of data collection and storage capabilities in recent decades,
abundant data have been accumulated in geotechnical engineering fields, providing …

[HTML][HTML] Prediction of the Permeability Tensor of Marine Clayey Sediment during Cyclic Loading and Unloading of Confinement Pressure Using Physical Tests and …

P Cui, J Zhou, R Gao, Z Fan, Y Jiang, H Liu, Y Zhang… - Water, 2024 - mdpi.com
In this study, a method was introduced to validate the presence of a Representative
Elementary Volume (REV) within marine clayey sediment containing cracks during cyclic …

[HTML][HTML] Evaluation of the Changes in the Strength of Clay Reinforced with Basalt Fiber Using Artificial Neural Network Model

Y Aslan Topçuoğlu, ZB Duranay, Z Gürocak - Applied Sciences, 2024 - mdpi.com
In this research, the impact of basalt fiber reinforcement on the unconfined compressive
strength of clay soils was experimentally analyzed, and the collected data were utilized in an …

The role of calcium-based additives in bentonite stabilization: a comparative evaluation

B Alibrahim, AHB Garoushi, E Uygar - Arabian Journal for Science and …, 2024 - Springer
Waste recycling plays a critical role in addressing environmental challenges by conserving
natural resources, reducing pollution, and mitigating the environmental impact of human …

Predicting the temperature-dependent long-term creep mechanical response of silica sand-textured geomembrane interfaces based on physical tests and machine …

Z Chao, H Wang, H Hu, T Ding, Y Zhang - Materials, 2023 - mdpi.com
Preciously assessing the creep mechanical response of sand–geomembrane interfaces is
vital for the design of relevant engineering applications, which is inevitable to be influenced …

Improving hydraulic conductivity prediction of bentonite using machine learning with generative adversarial network-based data augmentation

X Shi, P Zhang, J Feng, K Xu, Z Fang, J Tian… - Construction and Building …, 2025 - Elsevier
Accurately predicting the hydraulic conductivity (k) of compacted bentonite is essential for
modeling the thermo-hydro-mechanical-chemical processes of bentonite barriers in high …

Representative sample size for estimating saturated hydraulic conductivity via machine learning: A proof‐of‐concept study

A Ahmadisharaf, R Nematirad… - Water Resources …, 2024 - Wiley Online Library
Abstract Machine learning (ML) has been extensively applied in various disciplines.
However, not much attention has been paid to data heterogeneity in databases and number …

Predicting the Friction Angle of Bangkok Sand Using State Parameter and Neural Network

S Youwai, K Wongsala - Geotechnical and Geological Engineering, 2024 - Springer
Accurate determination of the friction angle of sand is crucial for foundation design. Existing
research lacks a comprehensive method to ascertain the friction angle specifically for …

Simulation of Leachate Movement from Clay Geosynthetic Liners Using a Laboratory Model

A Raei, M Shayannejad - Agricultural Research, 2024 - Springer
Landfilling is a widely used disposal of waste process that involves safely disposing of solid
waste on land. Its main goals are to eliminate health and environmental risks, minimize …

Enhancing Geosynthetic Clay Liner Performance through Modified Bentonite: A Molecular Dynamics Study

J Lu, T Wang, J Ma - Cosmic Journal of Biology, 2024 - journals.cosmic.edu.pk
Geosynthetic clay liners (GCLs) are crucial components in waste containment systems,
offering effective barriers against fluid migration in landfills and other environmental …