Prediction of consolidation behavior of modified clayey soil reinforced with artificial geo-fibers using explainable artificial intelligence
Journal
Chemometrics and Intelligent Laboratory Systems
ISSN
0169-7439
Date Issued
2026-04
Author(s)
Mohammed Faisal Noaman
Moinul Haq
Sanjog Chhetri Sapkota
Mehboob Anwer Khan
Kausar Ali
Abstract
The present study illustrates an experimental, machine learning (ML), and explainable artificial intelligence integrated framework for the prediction of swelling pressure and consolidation characteristics of polypropylene geo-fiber (PPGF) reinforced clayey soil.
A dataset of laboratory consolidation tests that included PPGF content, coefficient of consolidation (Cv), coefficient of compressibility (av), compression index (Cc), coefficient of volume change (mv), settlement (S), and swelling pressure values (ps) was compiled.
The experimental observations revealed that the Cc, mv, and S was averagely decreased by about 39.5%, 45.31%, and 90%, respectively, at the optimum PPGF content of 0.3%, thus demonstrating the effectiveness of reinforcing fibers in restraining time-dependent deformation. Six machine learning models, including KNN, SVM, ANN, DT, RF, and XGB, were developed using five folds cross-validation.
The XGB regressor proved to have the best predictive performances, having an R2 of 0.994 (with RMSE of 3.14) on training and generalizability in testing, with an R2 of 0.913 (having RMSE of 14.05).
The remaining models demonstrated comparatively weaker performance, with ANN and DT exhibiting pronounced overfitting, while KNN and SVM failed to adequately capture the nonlinear swelling response of the gels.
The XAI analysis using SHAP indicates that polypropylene geofiber content is the most influential factor governing swelling pressure, followed by mv and soil compressibility.
An interactive graphical user interface was built based on the optimized XGB model to predict and visualize swelling pressure in real time from given user inputs.
The proposed model integrates experimental validation with robust predictive capability and interpretability, and is complemented by a user-friendly interface and a reliable decision-support system for geotechnical design and soil improvement.
A dataset of laboratory consolidation tests that included PPGF content, coefficient of consolidation (Cv), coefficient of compressibility (av), compression index (Cc), coefficient of volume change (mv), settlement (S), and swelling pressure values (ps) was compiled.
The experimental observations revealed that the Cc, mv, and S was averagely decreased by about 39.5%, 45.31%, and 90%, respectively, at the optimum PPGF content of 0.3%, thus demonstrating the effectiveness of reinforcing fibers in restraining time-dependent deformation. Six machine learning models, including KNN, SVM, ANN, DT, RF, and XGB, were developed using five folds cross-validation.
The XGB regressor proved to have the best predictive performances, having an R2 of 0.994 (with RMSE of 3.14) on training and generalizability in testing, with an R2 of 0.913 (having RMSE of 14.05).
The remaining models demonstrated comparatively weaker performance, with ANN and DT exhibiting pronounced overfitting, while KNN and SVM failed to adequately capture the nonlinear swelling response of the gels.
The XAI analysis using SHAP indicates that polypropylene geofiber content is the most influential factor governing swelling pressure, followed by mv and soil compressibility.
An interactive graphical user interface was built based on the optimized XGB model to predict and visualize swelling pressure in real time from given user inputs.
The proposed model integrates experimental validation with robust predictive capability and interpretability, and is complemented by a user-friendly interface and a reliable decision-support system for geotechnical design and soil improvement.