Physics-Informed Neural Network Framework for Predicting Compressive Strength of Coconut Fiber Reinforced Concrete
Abstract
Abstract Accurate prediction of compressive strength in coconut fiber reinforced concrete (CFRC) is essential for sustainable infrastructure design. While data-driven machine learning models offer high predictive accuracy, they often violate fundamental physical laws of cement hydration and composite mechanics, leading to unreliable extrapolations. This study presents a novel Physics-Informed Neural Network (PINN) framework that explicitly encodes concrete material physics including cement hydration kinetics, Abrams’ water-cement ratio law, recycled coarse aggregate (RCA) degradation effects, and fiber reinforcement efficiency, into the learning architecture. Using a comprehensive dataset of 586 experimental records compiled from 30 independent studies, the proposed PINN architecture integrates physics-engineered features (hydration factors, Abrams indices, fiber efficiency parameters, and RCA reduction coefficients) with a deep neural network regularized by physical monotonicity constraints. The PINN achieved an \(\:{R}^{2}\) of 0.9482, RMSE of 3.89 MPa, and MAPE of 8.69%, demonstrating comparable accuracy to gradient boosting (\(\:{R}^{2}=0.9673\)) while ensuring complete compliance with age-monotonicity constraints and significantly reduced violations of water-cement ratio and RCA replacement laws. SHAP-based explainability analysis reveals that the PINN correctly captures the diminishing returns of fiber content beyond optimal dosage (1.5–2.0%), consistent with…
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