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

• Published in: Neural computing & applications Vol. 36; no. 22; pp. 13665 - 13687
• Main Authors: Rahman, Md Asif, Zhang, Tianjie, Lu, Yang
• Format: Journal Article
• Language: English
• Published: London Springer London 01.08.2024; Springer Nature B.V
• Subjects: Accuracy; Artificial Intelligence; Boundary conditions; Cement hydration; Cement paste; Computational Biology/Bioinformatics; Computational Science and Engineering; Computer Science; Data Mining and Knowledge Discovery; Heat generation; Hydration; Image Processing and Computer Vision; Kinetics; Machine learning; Material properties; Neural networks; Original Article; Partial differential equations; Physics; Prediction models; Probability and Statistics in Computer Science; Deep learning; Cement hydration kinetics; Physics-informed neural network; Cement-based materials; Hydration temperature
• ISSN: 0941-0643; 1433-3058
• DOI: 10.1007/s00521-024-09791-y

Cement hydration kinetics, characterized by heat generation in early-age concrete, poses a modeling challenge. This work proposes a physics-informed neural network (PINN) named PINN-CHK designed for cement hydration kinetics, to predict 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 (PDEs), and 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 predicting 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.