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Language: search.filters.language.EnglishSubject: search.filters.subject.Machine learning

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    Prediction of consolidation behavior of modified clayey soil reinforced with artificial geo-fibers using explainable artificial intelligence
    (Elsevier BV, 2026-04)
    Mohammed Faisal Noaman
    ;
    Moinul Haq
    ;
    Sanjog Chhetri Sapkota
    ;
    Mehboob Anwer Khan
    ;
    Kausar Ali
    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.
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    Delamination, frequency, and bending analysis of GPLRC curved panel with initial crack via machine learning and three-dimensional layerwise theory
    (Elsevier BV, 2025-12)
    Xinrong Cao
    ;
    Xiaohong Yang
    ;
    Linyuan Fan
    ;
    HABIBI, MOSTAFA  
    ;
    Ibrahim Albaijan
    In the present study, the thermal stability of graphene-reinforced composite laminates (GPL-RC) with diverse functional gradients and width delamination layers is examined. In this regard, various models of laminated GPL-RC are considered with different geometrical and material parameters. Utilizing the physics-informed neural networks (PINN), we calculate the energy release rate (ERR) at the cleavage boundary, aiming to gauge cleavage growth potential. This study also delves effects of various graphene reinforcement distributions and delamination configurations on the vibrational attributes of delaminated GPL-RC sheets, with an emphasis on pre/post heat bending modalities. Solutions are grounded in the third-order shear strain theory (TSDT), integrating von Karman geometric nonlinearity. Using the principle of minimal potential energy, the nonlinear equilibrium equations are tackled using PINN. Theoretical insights obtained are verified via a comparison to other published studies. Notably, parametric experiments indicate that the ERR in the FGX configuration in which most reinforcement material located adjacent to the upper and lower surfaces of the plate, is double that of the FGA, in which most reinforcement material adjacent to the lower surface of the plate. Moreover, while the FGX sheet's fundamental frequency surpasses other graphene configurations at the primary temperature, its natural frequency in the post-buckling modality is notably the least compared to the entire sample set.