Data‐driven dynamic mode decomposition framework for spatio‐temporal prediction of concrete chloride ingress

Prediction of concrete chloride ingress under varying environmental conditions is computationally demanding, particularly when mesostructural effects are considered. Uncertainties in service history and material properties further limit conventional models. This study develops a data‐driven dynamic mode decomposition framework for efficient prediction. It decomposes spatio‐temporal chloride concentration data into eigenmodes with temporal coefficients for accurate reconstruction and extrapolation. Its performance is demonstrated under constant, annual cyclic, and multi‐frequency boundary conditions. The reduced‐order representation cuts data storage by over 99% and enhances computational efficiency by over 91%. Sensitivity analyses indicate higher accuracy when input data are collected after long‐term chloride ingress and covers sufficient boundary cycles. Linear transformations of surface concentration fluctuations can be directly mapped to temporal coefficients of corresponding oscillatory modes. An analytical model expressing chloride profiles as an explicit function of depth and time is derived, applicable to all scenarios predictable by the proposed method.