Prediction of mechanical and durability properties of PET and rice husk ash concrete using an explainable hybrid machine learning framework

Abstract

The growing need for sustainable construction materials has led to increasing interest in concrete made with recycled and waste-derived components such as polyethylene terephthalate (PET) and rice husk ash (RHA). Accurately predicting the mechanical and durability properties of such materials remains challenging due to the complex interactions among mix constituents. To address this, this study develops a hybrid machine learning framework that combines a TFT-inspired attention-based feature interaction module with XGBoost, optimized using the Aquila Optimizer (AO). The model is further enhanced with Bayesian uncertainty quantification through Monte Carlo Dropout and dual explainable AI techniques (SHAP and LIME) to improve transparency and reliability. The model is trained on an experimental dataset of ten concrete mix designs, which is expanded using Gaussian Noise Injection, SMOTER, and Conditional GAN-based augmentation to improve learning robustness. It predicts six key properties: compressive strength, splitting tensile strength, flexural strength, water absorption, modulus of elasticity, and rapid chloride permeability. The proposed framework achieves high predictive accuracy (R² = 0.9992, RMSE = 0.00205, MAE = 0.0009), outperforming benchmark models. Interpretation results indicate that PET content and cement proportion are the most influential variables, while the uncertainty analysis provides reliable confidence bounds for each prediction. The model is specifically applicable to concrete properties at a curing age of 90 days. The TFT component is used in a non-temporal manner to capture feature interactions in tabular data, making the framework suitable for complex material systems where interpretability and prediction reliability are essential.