35 Years of (AI) in geotechnical engineering: state of the art

AM Ebid - Geotechnical and Geological Engineering, 2021 - Springer
It was 35 years ago since the first usage of Artificial Intelligence (AI) technique in
geotechnical engineering, during those years many (AI) techniques were developed based …

A scientometrics review of soil properties prediction using soft computing approaches

J Khatti, KS Grover - Archives of Computational Methods in Engineering, 2024 - Springer
In this world, several types of soils are available with their different engineering properties.
Determining each soil's engineering properties is difficult because the laboratory procedures …

Data utilization and partitioning for machine learning applications in civil engineering

AE Ebid, AF Deifalla, KC Onyelowe - International Conference on …, 2023 - Springer
abstract Machine learning (ML) has revolutionized civil engineering by enabling the analysis
of vast amounts of data to extract valuable insights and improve design practices. The …

[PDF][PDF] A study of relationship among correlation coefficient, performance, and overfitting using regression analysis

J Khatti, K Grover - Int. J. Sci. Eng. Res, 2022 - researchgate.net
The correlation coefficient is the method for presenting the relationship between features
and labels. The present research work aims to map a relationship among correlation …

[PDF][PDF] Determination of suitable hyperparameters of artificial neural network for the best prediction of geotechnical properties of soil

J Khatti, KS Grover - Int J Res Appl Sci Eng Technol, 2022 - academia.edu
The artificial neural network is robust in predicting soil properties. The present study aims to
determine the suitable hyperparameters such as number of hidden layers, neurons, and …

Predicting the chemical and mechanical properties of gypseous soils using different simulation technics

A Mohammed, RA Hummadi, YI Mawlood - Acta Geotechnica, 2022 - Springer
Gypseous soils are soils that contain sufficient quantities of gypsum that are considered
collapsible soil. The present study's objective is to predict the shear strength parameters (c …

[PDF][PDF] Prediction of collapse potential for gypseous sandy soil using ANN technique

AM Najemalden, SW Ibrahim… - Journal of Engineering …, 2020 - jestec.taylors.edu.my
The present study illustrates the efficiency of the Artificial Neural Network to model the
relationship between the collapse potential of gypseous sandy soil with the soil parameters …

Comparing nonlinear regression analysis and artificial neural networks to predict geotechnical parameters from standard penetration test

MA Benbouras, RM Kettab, H Zedira… - Urbanism. Arhitectura …, 2018 - ceeol.com
At the beginning the twenty-first century, a lot of high-level methods have become‎ available
in geotechnical engineering in order to deal with the complexity and‎ heterogeneity …

[PDF][PDF] PREDICTION OF COMPACTION PARAMETERS OF SOIL USING GA AND PSO OPTIMIZED RELEVANCE VECTOR MACHINE (RVM).

J Khatti, KS Grover - ICTACT Journal on Soft Computing, 2023 - ictactjournals.in
The present research introduces the best architectural relevance vector machine (RVM)
model for predicting the compaction parameters of soil. The two types of RVM models, ie …

DETERMINATION OF THE OPTIMUM PERFORMANCE AI MODEL AND METHODOLOGY TO PREDICT THE COMPACTION PARAMETERS OF SOILS.

J Khatti, KS Grover - ICTACT Journal on Soft Computing, 2022 - search.ebscohost.com
This technical article helps identify the optimum performance AI model for predicting
compaction parameters of soil. A comparative study is mapped between regression analysis …