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 …

Prediction of rapid chloride penetration resistance of metakaolin based high strength concrete using light GBM and XGBoost models by incorporating SHAP analysis

AA Alabdullah, M Iqbal, M Zahid, K Khan… - … and Building Materials, 2022 - Elsevier
This study investigates the non-linear capabilities of two machine learning prediction
models, namely Light GBM and XGBoost, for predicting the values of Rapid Chloride …

ANN-based swarm intelligence for predicting expansive soil swell pressure and compression strength

FE Jalal, M Iqbal, WA Khan, A Jamal, K Onyelowe… - Scientific Reports, 2024 - nature.com
This research suggests a robust integration of artificial neural networks (ANN) for predicting
swell pressure and the unconfined compression strength of expansive soils (P s UCS-ES) …

Comparative analysis of statistical and machine learning techniques for rice yield forecasting for Chhattisgarh, India

A Satpathi, P Setiya, B Das, AS Nain, PK Jha, S Singh… - Sustainability, 2023 - mdpi.com
Crop yield forecasting before harvesting is critical for the creation, implementation, and
optimization of policies related to food safety as well as for agro-product storage and …

Hybrid soft computing models for predicting unconfined compressive strength of lime stabilized soil using strength property of virgin cohesive soil

IT Bahmed, J Khatti, KS Grover - Bulletin of Engineering Geology and the …, 2024 - Springer
This work introduces an optimal performance model for predicting the unconfined
compressive strength (UCS) of lime-stabilized soil using the machine (ensemble tree (ET) …

Predicting the thermal conductivity of soils using integrated approach of ANN and PSO with adaptive and time-varying acceleration coefficients

N Kardani, A Bardhan, P Samui, M Nazem… - International Journal of …, 2022 - Elsevier
This study aims to propose hybrid adaptive neuro swarm intelligence (HANSI) techniques for
predicting the thermal conductivity of unsaturated soils. The novel contribution is made by …

A survey on the optimization of artificial neural networks using swarm intelligence algorithms

BAS Emambocus, MB Jasser, A Amphawan - IEEE access, 2023 - ieeexplore.ieee.org
Artificial Neural Networks (ANNs) are becoming increasingly useful in numerous areas as
they have a myriad of applications. Prior to using ANNs, the network structure needs to be …

Soft computing techniques for the prediction of concrete compressive strength using Non-Destructive tests

PG Asteris, AD Skentou, A Bardhan, P Samui… - … and Building Materials, 2021 - Elsevier
This study presents a comparative assessment of conventional soft computing techniques in
estimating the compressive strength (CS) of concrete utilizing two non-destructive tests …

Predicting ground vibration during rock blasting using relevance vector machine improved with dual kernels and metaheuristic algorithms

Y Fissha, J Khatti, H Ikeda, KS Grover, N Owada… - Scientific Reports, 2024 - nature.com
The ground vibration caused by rock blasting is an extremely hazardous outcome of the
blasting operation. Blasting activity has detrimental effects on both the ecology and the …

Prediction of UCS of fine-grained soil based on machine learning part 2: comparison between hybrid relevance vector machine and Gaussian process regression

J Khatti, KS Grover - Multiscale and Multidisciplinary Modeling …, 2024 - Springer
The present research employs the models based on the relevance vector machine (RVM)
approach to predict the unconfined compressive strength (UCS) of the cohesive virgin (fine …