Exploring time series models for landslide prediction: a literature review DOI Creative Commons
Kyrillos M. P. Ebrahim, Ali Fares, Nour Faris

et al.

Geoenvironmental Disasters, Journal Year: 2024, Volume and Issue: 11(1)

Published: Sept. 5, 2024

Language: Английский

Reliability Analysis of External Stabilities of Reinforced Soil Wall Using Monte Carlo Simulation and an Efficient Hybrid Artificial Neural Network Paradigm DOI
Sudeep Kumar, Avijit Burman

Transportation Infrastructure Geotechnology, Journal Year: 2025, Volume and Issue: 12(1)

Published: Jan. 1, 2025

Language: Английский

Citations

0
Comparative assessment of machine learning models for landslide susceptibility mapping: a focus on validation and accuracy DOI Creative Commons
Mohamed M. Abdelkader, Árpád Csámer

Natural Hazards, Journal Year: 2025, Volume and Issue: unknown

Published: March 13, 2025

Language: Английский

Citations

0
Developing advanced datadriven framework to predict the bearing capacity of piles on rock DOI Creative Commons
Kennedy C. Onyelowe, Shadi Hanandeh, Viroon Kamchoom‬

et al.

Scientific Reports, Journal Year: 2025, Volume and Issue: 15(1)

Published: April 1, 2025

Developing accurate predictive models for pile bearing capacity on rock is crucial optimizing foundation design and ensuring structural stability. This research presents an advanced data-driven framework that integrates multiple machine learning algorithms to predict the of piles based geotechnical in-situ test parameters. A comprehensive dataset comprising key influencing factors such as dimensions, geological characteristics, penetration resistance was utilized train validate various models, including Kstar, M5Rules, ElasticNet, XNV, Decision Trees. The Taylor diagram statistical evaluations demonstrated superiority proposed in capturing complex nonlinear relationships, with high correlation coefficients low root mean square errors indicating robust capabilities. Sensitivity analyses using Hoffman Gardener's approach SHAP values identified most influential parameters, revealing resistance, embedment depth, conditions significantly impact capacity. findings underscore effectiveness engineering applications, offering a reliable efficient alternative traditional empirical analytical methods. developed provides engineers practitioners powerful tool improving accuracy, reducing uncertainties, construction practices. Future should focus expanding diverse exploring hybrid modeling techniques enhance prediction accuracy further.

Language: Английский

Citations

0
Micro-macro spatiotemporal multi-graph network model for landslide displacement prediction DOI

Z. Wang,

Xiangwei Fang,

Chunni Shen

et al.

Engineering Analysis with Boundary Elements, Journal Year: 2025, Volume and Issue: 176, P. 106264 - 106264

Published: April 12, 2025

Citations

0
Geospatial SHAP interpretability for urban road collapse susceptibility assessment: a case study in Hangzhou, China DOI Creative Commons

Bofan Yu,

Hui Li, Huaixue Xing

et al.

Geomatics Natural Hazards and Risk, Journal Year: 2025, Volume and Issue: 16(1)

Published: April 15, 2025

Language: Английский

Citations

0
Short-Sequence Machine Learning Framework for Predicting Constitutive Relationships of Sand DOI Creative Commons

Xiangchen Yao,

Shuqi Ma,

Bo Li

et al.

Geotechnical and Geological Engineering, Journal Year: 2025, Volume and Issue: 43(2)

Published: Jan. 11, 2025

Language: Английский

Citations

0
Experimental and Computational Response of Relative Density of Soil of Katihar, India DOI

Rashid Mustafa

Indian geotechnical journal, Journal Year: 2025, Volume and Issue: unknown

Published: Feb. 7, 2025

Language: Английский

Citations

0
Multiscale progressive 3D geological modeling based on isochronous stratigraphy identification in urban underground space DOI
You Zhang,

He Lingling,

Yu‐Yong Jiao

et al.

Bulletin of Engineering Geology and the Environment, Journal Year: 2025, Volume and Issue: 84(4)

Published: March 6, 2025

Language: Английский

Citations

0
Application of machine learning in early warning system of geotechnical disaster: a systematic and comprehensive review DOI Creative Commons
Shan Lin,

Zenglong Liang,

Hongwei Guo

et al.

Artificial Intelligence Review, Journal Year: 2025, Volume and Issue: 58(6)

Published: March 17, 2025

Language: Английский

Citations

0
Risk Analysis of Pile Foundations Using an Improved Hybrid Ensemble Paradigm Coupled with Monte Carlo and Subset Simulations DOI
Subodh Kumar Suman, Shiva Shankar Choudhary, Avijit Burman

et al.

Transportation Infrastructure Geotechnology, Journal Year: 2025, Volume and Issue: 12(4)

Published: March 18, 2025

Language: Английский

Citations

0