Liquefaction potential assessment of soils using machine learning techniques: a state-of-the-art review from 1994–2021

K Jas, GR Dodagoudar - International Journal of Geomechanics, 2023 - ascelibrary.org
Abstract Machine learning (ML) has emerged as a powerful tool for prediction of systems
behavior in many engineering disciplines. A few applications of ML techniques are available …

Performance and emission characteristics of a CI engine using nano particles additives in biodiesel-diesel blends and modeling with GP approach

M Ghanbari, G Najafi, B Ghobadian, T Yusaf… - Fuel, 2017 - Elsevier
The performance and the exhaust emissions of a diesel engine operating on nano-diesel-
biodiesel blended fuels has been investigated. Multi wall carbon nano tubes (CNT)(40, 80 …

Development of empirical models with high accuracy for estimation of drag coefficient of flow around a smooth sphere: An evolutionary approach

R Barati, SAAS Neyshabouri, G Ahmadi - Powder Technology, 2014 - Elsevier
An accurate correlation for the smooth sphere drag coefficient with wide range of
applicability is a useful tool in the field of particle technology. The present study focuses on …

Assessment of soil liquefaction based on capacity energy concept and multivariate adaptive regression splines

W Zhang, ATC Goh, Y Zhang, Y Chen, Y **ao - Engineering Geology, 2015 - Elsevier
Soil liquefaction is one of the most complicated phenomena to assess in geotechnical
earthquake engineering. The procedures that have been developed to determine the …

Predicting earthquake-induced soil liquefaction based on a hybridization of kernel Fisher discriminant analysis and a least squares support vector machine: a multi …

ND Hoang, DT Bui - Bulletin of Engineering Geology and the Environment, 2018 - Springer
Assessment of the earthquake-induced liquefaction potential is a critical concern in design
processes of construction projects. This study proposes a novel soft computing model with a …

Comparison of statistical and machine learning methods in modelling of data with multicollinearity

A Garg, K Tai - International Journal of Modelling …, 2013 - inderscienceonline.com
Multicollinearity occurs in a dataset due to correlation between the predictors. Models
derived from such data without a check on multicollinearity may lead to erroneous system …

Application of dimensional analysis and multi-gene genetic programming to predict the performance of tunnel boring machines

M Kazemi, R Barati - Applied Soft Computing, 2022 - Elsevier
An accurate prediction of tunnel boring machine (TBM) performance is one of the complex
and crucial issues encountered frequently in tunnel construction, which is the aim of the …

Probabilistic capacity energy-based machine learning models for soil liquefaction reliability analysis

Z Zhao, W Duan, G Cai, M Wu, S Liu, AJ Puppala - Engineering Geology, 2024 - Elsevier
The energy-based method has been widely employed to evaluate soil liquefaction potential.
A recent trend is to develop machine learning (ML) models to predict capacity energy …

Artificial intelligence in geotechnical engineering: applications, modeling aspects, and future directions

MA Shahin - Metaheuristics in water, geotechnical and transport …, 2013 - books.google.com
Geotechnical engineering deals with materials (eg, soil and rock) that, by their very nature,
exhibit varied and uncertain behavior due to the imprecise physical processes associated …

[HTML][HTML] Energy-based numerical models for assessment of soil liquefaction

AH Alavi, AH Gandomi - Geoscience Frontiers, 2012 - Elsevier
This study presents promising variants of genetic programming (GP), namely linear genetic
programming (LGP) and multi expression programming (MEP) to evaluate the liquefaction …