Cited by Predicting the hardgrove grindability index using interpretable decision tree-based machine learning models

Prediction of venous clinical severity score in yoga practitioners and non-practitioners using discriminant analysis and metaheuristic algorithms DOI

Fengcai Wang,

Wang Yan Fei

Expert Systems with Applications, Journal Year: 2025, Volume and Issue: unknown, P. 127272 - 127272

Published: March 1, 2025

Language: Английский

Citations

Advancing overbreak prediction in drilling and blasting tunnel using MVO, SSA and HHO-based SVM models with interpretability analysis DOI

Yulin Zhang, Jian Zhou,

Jialu Li

et al.

Geomechanics and Geophysics for Geo-Energy and Geo-Resources, Journal Year: 2025, Volume and Issue: 11(1)

Published: May 22, 2025

Language: Английский

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Innovative Data-Driven Machine Learning Approaches for Predicting Sandstone True Triaxial Strength DOI

Rui Zhang, Jian Zhou, Zhenyu Wang

et al.

Applied Sciences, Journal Year: 2024, Volume and Issue: 14(17), P. 7855 - 7855

Published: Sept. 4, 2024

Given the critical role of true triaxial strength assessment in underground rock and soil engineering design construction, this study explores sandstone using data-driven machine learning approaches. Fourteen distinct test datasets were collected from existing literature randomly divided into training (70%) testing (30%) sets. A Multilayer Perceptron (MLP) model was developed with uniaxial compressive (UCS, σc), intermediate principal stress (σ2), minimum (σ3) as inputs maximum (σ1) at failure output. The optimized Harris hawks optimization (HHO) algorithm to fine-tune hyperparameters. By adjusting structure activation function characteristics, final made continuously differentiable, enhancing its potential for numerical analysis applications. Four HHO-MLP models different functions trained validated on set. Based comparison prediction accuracy meridian plane analysis, an high predictive meridional behavior consistent theoretical trends selected. Compared five traditional criteria (Drucker–Prager, Hoek–Brown, Mogi–Coulomb, modified Lade, Weibols–Cook), demonstrated superior performance both datasets. It successfully captured complete variation space, showing smooth continuous envelopes deviatoric planes. These results underscore model’s ability generalize across conditions, highlighting a powerful tool predicting geotechnical

Language: Английский

Citations

Predicting the hardgrove grindability index using interpretable decision tree-based machine learning models DOI

Yuxin Chen, Manoj Khandelwal, Moshood Onifade

et al.

Fuel, Journal Year: 2024, Volume and Issue: 384, P. 133953 - 133953

Published: Dec. 6, 2024

Language: Английский

Citations