Liquefaction Potential Assessment of Soils Using Machine Learning Techniques: A State-of-the-Art Review from 1994

Liquefaction Potential Assessment of Soils Using Machine Learning Techniques: A State-of-the-Art Review from 1994–2021

Published:2023-07 Issue:7 Volume:23 Page:
ISSN:1532-3641
Container-title:International Journal of Geomechanics
language:en
Short-container-title:Int. J. Geomech.

Author:

Jas Kaushik¹^ORCID,Dodagoudar G. R.²

Affiliation:

1. Doctoral Research Scholar, Dept. of Civil Engineering, Indian Institute of Technology Madras, Chennai, Tamil Nadu 600036, India (corresponding author). ORCID: .

2. Professor, Computational Geomechanics Laboratory, Dept. of Civil Engineering, Indian Institute of Technology Madras, Chennai, Tamil Nadu 600036, India.

Publisher

American Society of Civil Engineers (ASCE)

Subject

Soil Science

Link

https://ascelibrary.org/doi/pdf/10.1061/IJGNAI.GMENG-7788

Reference175 articles.

1. Evaluating Seismic Soil Liquefaction Potential Using Bayesian Belief Network and C4.5 Decision Tree Approaches

2. A hybrid computational intelligence approach to predict spectral acceleration;Akhani M.;Meas.,2019

3. Prediction of principal ground-motion parameters using a hybrid method coupling artificial neural networks and simulated annealing

4. Energy-based numerical models for assessment of soil liquefaction

5. New Ground-Motion Prediction Equations Using Multi Expression Programing

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