Assessment of resilient modulus of soil using hybrid extreme gradient boosting models DOI Creative Commons
Xiangfeng Duan

Scientific Reports, Journal Year: 2024, Volume and Issue: 14(1)

Published: Dec. 30, 2024

Accurate estimation of the soil resilient modulus (M

Language: Английский

Assessment of short and long-term pozzolanic activity of natural pozzolans using machine learning approaches DOI
Jitendra Khatti, Berivan Yılmazer Polat

Structures, Journal Year: 2024, Volume and Issue: 68, P. 107159 - 107159

Published: Sept. 1, 2024

Language: Английский

Citations

14
Enhancing unconfined compressive strength prediction in nano-silica stabilized soil: a comparative analysis of ensemble and deep learning models DOI
Ishwor Thapa, Sufyan Ghani

Modeling Earth Systems and Environment, Journal Year: 2024, Volume and Issue: 10(4), P. 5079 - 5102

Published: May 31, 2024

Language: Английский

Citations

10
Development of machine learning models for forecasting the strength of resilient modulus of subgrade soil: genetic and artificial neural network approaches DOI Creative Commons

Laiba Khawaja,

Usama Asif, Kennedy C. Onyelowe

et al.

Scientific Reports, Journal Year: 2024, Volume and Issue: 14(1)

Published: Aug. 6, 2024

Accurately predicting the Modulus of Resilience (MR) subgrade soils, which exhibit non-linear stress–strain behaviors, is crucial for effective soil assessment. Traditional laboratory techniques determining MR are often costly and time-consuming. This study explores efficacy Genetic Programming (GEP), Multi-Expression (MEP), Artificial Neural Networks (ANN) in forecasting using 2813 data records while considering six key parameters. Several Statistical assessments were utilized to evaluate model accuracy. The results indicate that GEP consistently outperforms MEP ANN models, demonstrating lowest error metrics highest correlation indices (R2). During training, achieved an R2 value 0.996, surpassing (R2 = 0.97) 0.95) models. Sensitivity SHAP (SHapley Additive exPlanations) analysis also performed gain insights into input parameter significance. revealed confining stress (21.6%) dry density (26.89%) most influential parameters MR. corroborated these findings, highlighting critical impact on predictions. underscores reliability as a robust tool precise prediction applications, providing valuable performance significance across various machine-learning (ML) approaches.

Language: Английский

Citations

9
Prediction method for the dynamic response of expressway lateritic soil subgrades on the basis of Bayesian optimization CatBoost DOI
Xuanjia Huang, Weizheng Liu, Qing Guo

et al.

Soil Dynamics and Earthquake Engineering, Journal Year: 2024, Volume and Issue: 186, P. 108943 - 108943

Published: Sept. 5, 2024

Language: Английский

Citations

9
Methodology for Constructing Explicit Stability Formulas for Hard Rock Pillars: Integrating Data-Driven Approaches and Interpretability Techniques DOI

Yingui Qiu,

Jian Zhou

Rock Mechanics and Rock Engineering, Journal Year: 2025, Volume and Issue: unknown

Published: Feb. 5, 2025

Language: Английский

Citations

1
Robust drought forecasting in Eastern Canada: Leveraging EMD-TVF and ensemble deep RVFL for SPEI index forecasting DOI
Masoud Karbasi, Mumtaz Ali, Aitazaz A. Farooque

et al.

Expert Systems with Applications, Journal Year: 2024, Volume and Issue: 256, P. 124900 - 124900

Published: July 30, 2024

Language: Английский

Citations

4
Interpretable Machine Learning for Predicting Heavy Metal Removal Efficiency in Electrokinetic Soil Remediation DOI
Mohammad Sadegh Barkhordari,

Nana Zhou,

Kechao Li

et al.

Journal of environmental chemical engineering, Journal Year: 2024, Volume and Issue: 12(6), P. 114330 - 114330

Published: Oct. 6, 2024

Language: Английский

Citations

4
Assessment on eco-solidified alkali residue reinforced soft soils for intelligent subgrade constructions DOI
Jianhua Li,

Zicheng Zhang,

Xu Liu

et al.

Transportation Geotechnics, Journal Year: 2025, Volume and Issue: unknown, P. 101516 - 101516

Published: Feb. 1, 2025

Language: Английский

Citations

0
An empirical-driven machine learning (EDML) approach to predict PPV caused by quarry blasting DOI Creative Commons
Panagiotis G. Asteris, Danial Jahed Armaghani

Bulletin of Engineering Geology and the Environment, Journal Year: 2025, Volume and Issue: 84(4)

Published: March 19, 2025

Language: Английский

Citations

0
Enhancing the Predictive Accuracy of Marshall Design Tests Using Generative Adversarial Networks and Advanced Machine Learning Techniques DOI
Usama Asif, Waseem Akhtar Khan,

Khawaja Atif Naseem

et al.

Materials Today Communications, Journal Year: 2025, Volume and Issue: unknown, P. 112379 - 112379

Published: March 1, 2025

Language: Английский

Citations

0