Physics-Informed neural network based inversion and prediction of natural chloride diffusion in uncracked and cracked concrete systems

Studying chloride diffusion in concrete is essential for predicting structural durability and designing corrosion-resistant materials and structures. While analytical models and finite element methods can simulate diffusion, they typically require large and high-quality datasets and do not possess advantages in parameter identification. Physics-Informed Neural Network, which integrates Fick 2nd law with initial and boundary conditions, offer a promising alternative. It not only replicates diffusion behavior accurately but also enhances the fitting of experimental data via a data-driven loss term and enable inverse estimation of diffusion related parameters. This paper outlines three key advantages of using this new method for problem-solving about chloride diffusion in concrete: (1) robustness to noise and low data requirements for one-dimensional inverse estimation of diffusion coefficients; (2) strategy integrates data, physics, and engineering insights for parameter inversion.; and (3) extended physics-informed frameworks with weak constraints for cracked concrete. Overall, Physics-Informed Neural Network provides a robust numerical tool for efficient durability assessment and the design of corrosion-resistant and resilient concrete structures. For self-healing concrete, the proposed framework effectively estimates diffusion coefficient in healed crack and accurately predicts long-term diffusion behavior, contributing to the optimal design and evaluation of self-healing materials.