Deep Learning-Based Porosity Prediction of Concrete Under Freeze–Heaving Conditions Using Strain Fields

Freeze-induced damage in concrete is governed by complex interactions between pore-scale phase transition and macroscopic mechanical response, while the underlying pore structure is typically difficult to observe directly. This study proposes an integrated framework for porosity inversion in concrete under freeze–heaving conditions, combining mechanical modeling, finite element simulation, and deep learning. A mechanics-based model is first developed to describe frost-heaving behavior in porous concrete, accounting for elastoplastic deformation of the matrix and partial volumetric expansion induced by pore water freezing. Based on this formulation, a parametric finite element model with randomly distributed pores is constructed to generate datasets linking pore characteristics to full-field deformation responses. Building upon these physics-consistent data, a deep learning framework is established to reconstruct pore distribution directly from three-component strain fields. The model employs a Vision Transformer backbone to capture global deformation patterns and incorporates a Kolmogorov–Arnold Network-based nonlinear mapping to enhance representation of the highly nonlinear inverse relationship. The results demonstrate that the proposed approach achieves accurate pore reconstruction and porosity prediction with stable convergence and satisfactory generalization performance across different porosity levels. The study provides a physically interpretable and computationally efficient pathway for linking deformation fields to internal pore structure, offering new potential for non-destructive characterization and durability assessment of concrete in cold-region environments.