Indirect hazard evaluation by the prediction of backbreak distance in the open pit mine using support vector regression and chicken swarm optimization DOI Creative Commons
Enming Li, Zongguo Zhang, Jian Zhou

et al.

Geohazard Mechanics, Journal Year: 2024, Volume and Issue: unknown

Published: Nov. 1, 2024

Language: Английский

Geotechnical Characterization and Stability Prediction of Nano-Silica-Stabilized Slopes: A Machine Learning Approach to Mitigating Geological Hazards DOI Creative Commons
Ishwor Thapa, Sufyan Ghani, Sunita Kumari

et al.

Transportation Infrastructure Geotechnology, Journal Year: 2025, Volume and Issue: 12(2)

Published: Feb. 1, 2025

Language: Английский

Citations

1
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
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
Landslide Susceptibility Assessment Using Recurrent Neural Network (RNN)—A Case of Chabahar and Konarak in Iran DOI
Vahid Isazade, Abdul Baser Qasimi,

Mahdi Safari Namivandi

et al.

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

Published: April 3, 2025

Language: Английский

Citations

0
ConToGCN: A landslide susceptibility assessment model considering contour topographic features in slope units using graph convolution network DOI Creative Commons

Jingru Ma,

Zhigang Han, Feng Liu

et al.

CATENA, Journal Year: 2025, Volume and Issue: 255, P. 109029 - 109029

Published: April 16, 2025

Language: Английский

Citations

0
Indirect hazard evaluation by the prediction of backbreak distance in the open pit mine using support vector regression and chicken swarm optimization DOI Creative Commons
Enming Li, Zongguo Zhang, Jian Zhou

et al.

Geohazard Mechanics, Journal Year: 2024, Volume and Issue: unknown

Published: Nov. 1, 2024

Language: Английский

Citations

1