A scientometrics review of soil properties prediction using soft computing approaches
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 …
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
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 …
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
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) …
swell pressure and the unconfined compression strength of expansive soils (P s UCS-ES) …
Soft computing techniques for the prediction of concrete compressive strength using Non-Destructive tests
This study presents a comparative assessment of conventional soft computing techniques in
estimating the compressive strength (CS) of concrete utilizing two non-destructive tests …
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
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 …
blasting operation. Blasting activity has detrimental effects on both the ecology and the …
Predicting the thermal conductivity of soils using integrated approach of ANN and PSO with adaptive and time-varying acceleration coefficients
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 …
predicting the thermal conductivity of unsaturated soils. The novel contribution is made by …
Comparative analysis of statistical and machine learning techniques for rice yield forecasting for Chhattisgarh, India
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 …
optimization of policies related to food safety as well as for agro-product storage and …
Prediction of UCS of fine-grained soil based on machine learning part 2: comparison between hybrid relevance vector machine and Gaussian process regression
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 …
approach to predict the unconfined compressive strength (UCS) of the cohesive virgin (fine …
Prediction of the seismic effect on liquefaction behavior of fine-grained soils using artificial intelligence-based hybridized modeling
Researchers in the past have reported significant uncertainties involved in evaluating the
risk of soil liquefaction using deterministic approaches. Therefore, to improve the accuracy …
risk of soil liquefaction using deterministic approaches. Therefore, to improve the accuracy …
A novel improved Harris Hawks optimization algorithm coupled with ELM for predicting permeability of tight carbonates
Tight carbonate reservoirs appear to be heterogeneous due to the patchy production of
various digenetic properties. Consequently, the permeability calculation of tight rocks is …
various digenetic properties. Consequently, the permeability calculation of tight rocks is …