Maximum probabilistic damage imaging using cross-correlation coefficient-based stress waves for quantifying concrete cracks

Probabilistic damage imaging (PDI) based on stress waves is effective for structural health monitoring of concrete. However, its accuracy is often degraded by material heterogeneity, complex wave propagation, and artifacts caused by the elliptical-contour assumption in conventional weighted PDI algorithms. This study proposes a cross-correlation-coefficient-based damage index combined with t-distribution hypothesis testing. This integration robustly extracts damage-sensitive features and reduces the influence of noise and anisotropy. We further develop a maximum PDI approach that replaces probability superposition with the pixel-wise maximum damage probability across sensing paths. Combined with iterative threshold-based segmentation, the method suppresses artifacts and delineates both the damage extent and peak damage locations. The framework is validated using reinforced concrete slabs with artificial cracks and a steel-reinforced concrete column subjected to quasi-static cyclic loading. The results show substantial improvements over conventional PDI. Localization errors are reduced by up to 92%, artifacts are eliminated, the damage region is captured with a 100% success rate, and image clarity and interpretability are markedly improved. Overall, the proposed framework provides a reliable, efficient, and accurate solution for quantitative damage visualization and real-time monitoring of heterogeneous reinforced concrete structures. It has strong potential to advance structural health monitoring in civil engineering.