Performance of Artificial Neural Network and Physics-Informed Neural Networks for Flexural Strength Using Sugarcane Bagasse Ash and Distilled Sewage Water

An increasing number of eco-conscious construction projects are looking to agricultural waste products, such as sugarcane bagasse ash (SCBA) and Distilled Sewage Water, to partially replace cement and Pure Water in their mixes. This study evaluates the predictive power of Artificial Neural Networks (ANNs) and Physics-Informed Neural Networks (PINNs) by examining the increase in flexural strength of SCBA-based concrete at 7, 14, and 28 days. The results of the experiment showed a steady increase in flexural strength with curing age, indicating constant hydration and good matrix densification. The nonlinear correlation between mix design factors and strength was anticipated using ANN and PINN methods and tested and validated through training, cross-validation (OOF), testing and full-dataset appraisals. The ANN demonstrated relatively high training accuracy, but it showed evidence of overtraining and poor generalisation, as indicated by negative R2 values on the validation and test datasets. Conversely, the PINN, where constraints are included in the loss function based on the physics, demonstrated a relatively higher stability and reduced error values (MSE, MAE, RMSE) on every data split. Reduced variance dispersion in PINN was demonstrated using residual analysis, and SHAP-based interpretability showed that cement content, percentage of SCBA and ratio of water-binder were the strongest predictors of flexural strength. Despite the fact that both models still need additional improvement to be more generalisable, the findings indicate that physics-informed learning can improve the robustness and interpretability of strength prediction. The suggested integrated experimental-PINN system presents a potential solution to the sustainable concrete performance modelling and the intelligent mix design optimisation.