A Review on Liquefaction Analysis Based on Stress and Energy Based Approaches Using SPT DOI

P. Aditya Sai Teja,

B. Vaishnavi,

K. Chandra Vishal

et al.

Published: Jan. 1, 2024

Language: Английский

Explainable machine learning model for liquefaction potential assessment of soils using XGBoost-SHAP DOI
Kaushik Jas, G. R. Dodagoudar

Soil Dynamics and Earthquake Engineering, Journal Year: 2022, Volume and Issue: 165, P. 107662 - 107662

Published: Nov. 30, 2022

Language: Английский

Citations

83
Evaluation and analysis of liquefaction potential of gravelly soils using explainable probabilistic machine learning model DOI
Kaushik Jas, Sujith Mangalathu, G. R. Dodagoudar

et al.

Computers and Geotechnics, Journal Year: 2024, Volume and Issue: 167, P. 106051 - 106051

Published: Jan. 8, 2024

Language: Английский

Citations

20
Differential Privacy in Geotechnical Engineering DOI Creative Commons
Takao Murakami, Stephen Wu, Jinzhang Zhang

et al.

Geodata and AI., Journal Year: 2025, Volume and Issue: unknown, P. 100004 - 100004

Published: Feb. 1, 2025

Language: Английский

Citations

2
A Scientometrics Review of Soil Properties Prediction Using Soft Computing Approaches DOI
Jitendra Khatti, Kamaldeep Singh Grover

Archives of Computational Methods in Engineering, Journal Year: 2023, Volume and Issue: 31(3), P. 1519 - 1553

Published: Nov. 24, 2023

Language: Английский

Citations

23
Cone penetration test-based assessment of liquefaction potential using machine and hybrid learning approaches DOI
Jitendra Khatti,

Yewuhalashet Fissha,

Kamaldeep Singh Grover

et al.

Multiscale and Multidisciplinary Modeling Experiments and Design, Journal Year: 2024, Volume and Issue: 7(4), P. 3841 - 3864

Published: April 26, 2024

Language: Английский

Citations

5
Prediction of shear strain and excess pore water pressure response in liquefiable sands under cyclic loading using deep learning model DOI Open Access
Kaushik Jas, Amalesh Jana, G. R. Dodagoudar

et al.

Japanese Geotechnical Society Special Publication, Journal Year: 2024, Volume and Issue: 10(46), P. 1729 - 1734

Published: Jan. 1, 2024

Language: Английский

Citations

5
Evaluation of empirical and machine learning models for predicting shear wave velocity of granular soils based on laboratory element tests DOI
Zohreh Mousavi, Meysam Bayat, Jun Yang

et al.

Soil Dynamics and Earthquake Engineering, Journal Year: 2024, Volume and Issue: 183, P. 108805 - 108805

Published: June 28, 2024

Language: Английский

Citations

5
Pseudo-static slope stability analysis using explainable machine learning techniques DOI
Abdul Waris Kenue, Sheikh Junaid Fayaz,

Alluri Harshith Reddy

et al.

Natural Hazards, Journal Year: 2024, Volume and Issue: unknown

Published: July 30, 2024

Language: Английский

Citations

5
Seismically Induced Liquefaction Potential Assessment by Different Artificial Intelligence Procedures DOI

Divesh Ranjan Kumar,

Pijush Samui,

Avijit Burman

et al.

Transportation Infrastructure Geotechnology, Journal Year: 2023, Volume and Issue: 11(3), P. 1272 - 1293

Published: Sept. 1, 2023

Language: Английский

Citations

12
Pathway to a fully data-driven geotechnics: Lessons from materials informatics DOI Open Access
Stephen Wu, Yu Otake, Yosuke Higo

et al.

SOILS AND FOUNDATIONS, Journal Year: 2024, Volume and Issue: 64(3), P. 101471 - 101471

Published: May 4, 2024

This paper elucidates the challenges and opportunities inherent in integrating data-driven methodologies into geotechnics, drawing inspiration from success of materials informatics. Highlighting intricacies soil complexity, heterogeneity, lack comprehensive data, discussion underscores pressing need for community-driven database initiatives open science movements. By leveraging transformative power deep learning, particularly feature extraction high-dimensional data potential transfer we envision a paradigm shift towards more collaborative innovative geotechnics field. The concludes with forward-looking stance, emphasizing revolutionary brought about by advanced computational tools like large language models reshaping

Language: Английский

Citations

4