PINN-CHK: Physics-informed neural network for high-fidelity prediction of early-age cement hydration kinetics

Abstract

Abstract Cement hydration kinetics, characterized by heat generation in early concrete stages, poses a modeling challenge. This study introduces the Physics-Informed Neural Network for Cement Hydration Kinetics (PINN-CHK) to investigate early-age temperature rises in cement paste. PINN-CHK leverages data-driven solutions to craft a high-fidelity prediction model, encompassing material properties and maturity functions in cement hydration. Trained on heated cement paste data, it simultaneously fits experimental results and underlying physics, yielding a mesh-free simulation. Incorporating governing partial differential equations, initial and boundary conditions into its loss function, PINN-CHK architecture undergoes rigorous benchmark testing, demonstrating unparalleled predictive accuracy compared to conventional deep learning methods. It excels in forecasting complete temperature fields during spatial-temporal cement hydration, achieving a remarkable relative L2 error as low as 0.00341. PINN-CHK achieves exceptional convergence and accuracy with only 5% of the training data, ushering in a new era in this crucial field. This innovative approach bridges the gap between theory and practice, offering an attractive alternative to conventional finite element solvers for enhanced comprehension of cement hydration kinetics and concrete maturity and strength development in cement-based materials.