- ISSN 0258-2724
- CN 51-1277/U
- EI Compendex
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- Indexed by Core Journals of China, Chinese S&T Journal Citation Reports
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| Citation: | SHAN Deshan, YU Zhongru, SUN Ronghui. Bridge Structural Health Monitoring Based on Physics-Informed Neural Networks: Research Advances and Review[J]. Journal of Southwest Jiaotong University, 2026, 61(3): 855-877. doi: 10.3969/j.issn.0258-2724.20260055
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Bridge structural health monitoring (BSHM) plays a critical role in ensuring the safe operation and extending the service life of bridges. However, the monitoring accuracy and engineering applicability of conventional physics-driven and data-driven BSHM methods are often constrained under the influence of complex operational environments, noise interference, data incompleteness, and model uncertainties. In recent years, physics-informed neural networks (PINNs) and generalized physics-informed machine learning (PIML) methods have developed rapidly, providing new ideas and technical means to overcome the limitations of traditional BSHM methods. The core idea of PINNs is to explicitly or implicitly embed physical prior knowledge, such as physical governing equations and boundary conditions, into deep neural networks, thereby guiding the model to satisfy physical consistency while learning data and improving generalization performance. The theoretical foundations of PINNs/PIML were systematically reviewed, and the advantages and disadvantages of typical physics-embedding strategies, including physical enhancement of feature spaces, physical model data augmentation, physics-informed network regularization, and physics-guided network architecture design, were comparatively analyzed. Focusing on typical tasks in BSHM, such as structural behavior modeling, parameter identification, signal decomposition and reconstruction, as well as damage detection and identification, the latest research advances of PINNs in the field of BSHM were systematically summarized. The main challenges and potential development directions faced by PINNs-based BSHM in practical engineering applications were discussed. With the continuous integration of deep learning methods and physical modeling strategies, PINNs are expected to become an important technical means in intelligent bridge operation and maintenance, providing support for improving bridge condition assessment capabilities and operation and maintenance decision-making levels.
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