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

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

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Efficient prediction of California bearing ratio in solid waste-cement-stabilized soil using improved hybrid extreme gradient boosting model

Materials Today Communications, Journal Year: 2025, Volume and Issue: 43, P. 111627 - 111627

Published: Jan. 15, 2025

Language: Английский

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Modeling an artificial neural network to estimate cement consumption in clayey waste-cement mixtures based on curing temperature, mechanical strength, and resilient modulus

Construction and Building Materials, Journal Year: 2025, Volume and Issue: 467, P. 140376 - 140376

Published: Feb. 12, 2025

Language: Английский

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Predicting the compressive strength of solid waste-cement stabilized compacted soil using machine learning model

Materials Today Communications, Journal Year: 2025, Volume and Issue: unknown, P. 111882 - 111882

Published: Feb. 1, 2025

Language: Английский

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Novel approaches in prediction of tensile strain capacity of engineered cementitious composites using interpretable approaches

REVIEWS ON ADVANCED MATERIALS SCIENCE, Journal Year: 2025, Volume and Issue: 64(1)

Published: Jan. 1, 2025

Abstract The performance and durability of conventional concrete (CC) are significantly influenced by its weak tensile strength strain capacity (TSC). Thus, the intrusion fibers in cementitious matrix forms ductile engineered composites (ECCs) that can cater to this area CC. Moreover, ECCs have become a reasonable substitute for brittle plain due their increased flexibility, ductility, greater TSC. prediction ECC is crucial without need laborious experimental procedures. achieve this, machine learning approaches (MLAs), namely light gradient boosting (LGB) approach, extreme (XGB) artificial neural network (ANN), k -nearest neighbor (KNN), were developed. data gathered from literature comprise input parameters which fiber content, length, cement, diameter, water-to-binder ratio, fly ash (FA), age, sand, superplasticizer, TSC as output utilized. assessment models gauged with coefficient determination ( R 2 ), statistical measures, uncertainty analysis. In addition, an analysis feature importance carried out further refinement model. result demonstrates ANN XGB perform well train test sets > 0.96. Statistical measures show all give fewer errors higher , depict robust performance. Validation via K -fold confirms showing correlation determination. reveals FA major contribution ECC. graphical user interface also developed help users/researchers will facilitate them estimate practical applications.

Language: Английский

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Applications of machine learning and traditional prediction techniques for estimating resilient modulus values of unbound granular materials incorporating reclaimed asphalt pavement (RAP) as a primary component

Construction and Building Materials, Journal Year: 2025, Volume and Issue: 473, P. 140900 - 140900

Published: March 28, 2025

Language: Английский

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Research on Pile Bearing Capacity Prediction Model Based on Optimized Random Forests

Sustainable civil infrastructures, Journal Year: 2025, Volume and Issue: unknown, P. 106 - 126

Published: Jan. 1, 2025

Language: Английский

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Optimizing biochar yield and composition prediction with ensemble machine learning models for sustainable production

Ain Shams Engineering Journal, Journal Year: 2024, Volume and Issue: 16(1), P. 103209 - 103209

Published: Dec. 28, 2024

Language: Английский

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