Bridge deterioration modeling plays a crucial role in infrastructure maintenance and lifespan prediction. Current methodologies evolve along three axes: (1) Mechanism‐driven models, which leverage structural mechanics and material degradation theories to provide detailed insights into the physical processes underlying deterioration, constrained by computational intensity; (2) probabilistic frameworks, which are used to quantify degradation uncertainties, effective for long‐term reliability yet insensitive to anomalies; and (3) data‐driven models, which are used for high‐dimensional pattern mining, limited by data dependency and interpretability barriers. The hybrid intelligence paradigm emerges as a transformative solution, integrating physical laws, stochastic processes, and machine learning. This review systematically evaluates contemporary techniques across computational efficiency, predictive robustness, and engineering applicability. In addition, comprehensive structural health monitoring of bridges is advancing through the investigation of deterioration mechanisms, the application of nondestructive damage detection tools, and the exploration of emerging technologies such as AI‐based tools, data fusion integrated with digital twin systems. Priority innovations should focus on (1) developing resilient data processing methods, (2) novel multisensor joint reconstruction algorithms that can effectively mitigate simultaneous data loss, (3) creating prescriptive analytics systems that synchronize real‐time structural responses with probabilistic multihazard simulations, and (4) incorporating attention mechanisms and robust recursive algorithms into time‐series models to better capture long‐term dependencies and mitigate error accumulation.
Yin et al. (2026) studied this question.