Physics-Informed Digital Twin for Climate-Induced Degradation Prediction in Aging Coastal Bridges
DOI:
https://doi.org/10.51903/19t45v40Keywords:
Bridge Degradation, Chloride-Induced Corrosion, Digital Twin, Physics-Informed Learning, Structural Health MonitoringAbstract
Climate change has accelerated the deterioration of aging coastal bridges by increasing chloride exposure and environmental variability, creating significant challenges for long-term Structural Health Monitoring. While Digital Twin technology enables real-time infrastructure monitoring, existing approaches often lack explicit representation of the physical mechanisms governing structural degradation. This study aims to develop a Physics-Informed Digital Twin framework by integrating Physics-Informed Neural Networks (PINNs) with Internet of Things (IoT)-based sensing for long-term prediction of climate-induced bridge deterioration. The proposed framework combines real-time sensor synchronization, chloride diffusion physics, and deep learning to improve predictive accuracy and physical consistency. The model achieved a coefficient of determination (R²) of 0.98, a Mean Absolute Error (MAE) of 0.041, a Root Mean Square Error (RMSE) of 0.056, and a Mean Absolute Percentage Error (MAPE) of 2.84%, while accurately simulating structural degradation over a 50-year service life under multiple coastal microclimate scenarios. The novelty of this study lies in the integration of physics-informed learning, chloride transport mechanisms, and Digital Twin technology into a unified predictive framework for coastal bridge deterioration assessment. This framework extends conventional Digital Twin systems from monitoring to physics-guided prognostic analysis, providing a reliable tool for predictive maintenance and infrastructure resilience. The findings demonstrate that incorporating governing physical laws into Digital Twin architectures substantially improves the robustness, accuracy, and engineering reliability of long-term degradation prediction for aging coastal bridges.
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Agbayani, V. E. T., Kee, S.-H., Monjardin, C. E. F., & Robles, K. P. V. (2026). Electrical Resistivity-Based Prediction of Corrosion-Affected Areas in Reinforced Concrete. Buildings, 16(4), 886. https://doi.org/10.3390/buildings16040886
Cao, H., Yu, J., & Duan, F. (2025). Condition-Based Maintenance in Complex Degradation Systems: A Review of Modeling Evolution, Multi-Component Systems, and Maintenance Strategies. Machines, 13(8), 714. https://doi.org/10.3390/machines13080714
Farea, A., Yli-Harja, O., & Emmert-Streib, F. (2024). Understanding Physics-Informed Neural Networks: Techniques, Applications, Trends, and Challenges. AI, 5(3), 1534–1557. https://doi.org/10.3390/ai5030074
Faris, N., Zayed, T., & Fares, A. (2025). Review of Condition Rating and Deterioration Modeling Approaches for Concrete Bridges. Buildings, 15(2), 219. https://doi.org/10.3390/buildings15020219
Fu, S., & Avdelidis, N. P. (2023). Prognostic and Health Management of Critical Aircraft Systems and Components: An Overview. Sensors, 23(19), 8124. https://doi.org/10.3390/s23198124
Hakimi, O., Liu, H., Abudayyeh, O., Houshyar, A., Almatared, M., & Alhawiti, A. (2023). Data Fusion for Smart Civil Infrastructure Management: A Conceptual Digital Twin Framework. Buildings, 13(11), 2725. https://doi.org/10.3390/buildings13112725
Huang, H., Xing, Z., Li, Z., Xing, X., Jiang, M., Huang, Q., Huang, C., Jiang, M., & Hui, D. (2026). Critical Review on Durable Concrete in Chloride-Containing Environments: Material Design, Monitoring, and Life-Cycle Management. Coatings, 16(5), 558. https://doi.org/10.3390/coatings16050558
In-Hwa, K., Song-Il, P., Na, I. H., Young, K. A., & Chul, K. Y. (2025). Ethical AI in the Metaverse: A Mixed-Methods Study on Design Innovation, Social Implications, and Fairness Challenges. International Journal of Graphic Design, 3(1), 53–65. https://doi.org/10.51903/ijgd.v3i1.2791
Kazemi, M. J., Rashidi, M., Kang, W.-H., & Siahkouhi, M. (2026). Toward Smart Railway Infrastructure Predictive and Optimised Maintenance Through Digital Twin (DT) System. Sensors, 26(8), 2333. https://doi.org/10.3390/s26082333
Khalid, S., Yazdani, M. H., Azad, M. M., Elahi, M. U., Raouf, I., & Kim, H. S. (2025). Advancements in Physics-Informed Neural Networks for Laminated Composites: A Comprehensive Review. Mathematics, 13(1), 17. https://doi.org/10.3390/math13010017
Krzywanski, J., Sosnowski, M., Grabowska, K., Zylka, A., Lasek, L., & Kijo-Kleczkowska, A. (2024). Advanced Computational Methods for Modeling, Prediction and Optimization—A Review. Materials, 17(14), 3521. https://doi.org/10.3390/ma17143521
Lee, T., Kim, D., Cho, S., & Kim, M. O. (2025). Advancements in Surface Coatings and Inspection Technologies for Extending the Service Life of Concrete Structures in Marine Environments: A Critical Review. Buildings, 15(3), 304. https://doi.org/10.3390/buildings15030304
Miller, T., Durlik, I., Łobodzińska, A., Dorobczyński, L., & Jasionowski, R. (2024). AI in Context: Harnessing Domain Knowledge for Smarter Machine Learning. Applied Sciences, 14(24), 11612. https://doi.org/10.3390/app142411612
Moravvej, M., & El-Badry, M. (2024). Reference-Free Vibration-Based Damage Identification Techniques for Bridge Structural Health Monitoring—A Critical Review and Perspective. Sensors, 24(3), 876. https://doi.org/10.3390/s24030876
Mousavi, V., Rashidi, M., Mohammadi, M., & Samali, B. (2024). Evolution of Digital Twin Frameworks in Bridge Management: Review and Future Directions. Remote Sensing, 16(11), 1887. https://doi.org/10.3390/rs16111887
Musa, A. A., Hussaini, A., Liao, W., Liang, F., & Yu, W. (2023). Deep Neural Networks for Spatial-Temporal Cyber-Physical Systems: A Survey. Future Internet, 15(6), 199. https://doi.org/10.3390/fi15060199
Piras, G., Agostinelli, S., & Muzi, F. (2024). Digital Twin Framework for Built Environment: A Review of Key Enablers. Energies, 17(2), 436. https://doi.org/10.3390/en17020436
Rabi, R. R., Vailati, M., & Monti, G. (2024). Effectiveness of Vibration-Based Techniques for Damage Localization and Lifetime Prediction in Structural Health Monitoring of Bridges: A Comprehensive Review. Buildings, 14(4), 1183. https://doi.org/10.3390/buildings14041183
Rincon, L. F., Moscoso, Y. M., Hamami, A. E., Matos, J. C., & Bastidas-Arteaga, E. (2024). Degradation Models and Maintenance Strategies for Reinforced Concrete Structures in Coastal Environments under Climate Change: A Review. Buildings, 14(3), 562. https://doi.org/10.3390/buildings14030562
Saeheaw, T. (2025). Hybrid Gaussian Process Regression Models for Accurate Prediction of Carbonation-Induced Steel Corrosion in Cementitious Mortars. Buildings, 15(14), 2464. https://doi.org/10.3390/buildings15142464
Sun, J., He, J., Zhou, G., Yang, J., Sun, X., & Teng, S. (2026). The Fusion Mechanism and Prospective Application of Physics-Informed Machine Learning in Bridge Lifecycle Health Monitoring. Infrastructures, 11(1), 16. https://doi.org/10.3390/infrastructures11010016
Syafril, M., Arief, S., & Pangaribuan, J. D. (2025). Application of Digital Twin Technology for Real-Time Monitoring and Predictive Maintenance of Coastal Bridges under Climate Change Scenarios. Jurnal Rekayasa Sipil Dan Arsitektur, 1(2), 39–51. https://doi.org/10.51903/5fkqb467
Tommy, A. S., & Jaya, R. P. (2025). Integration of AI and Digital Twin Technology for Smart Infrastructure Management in Urban Cities. Civil Engineering Science and Technology, 1(1), 45–62. https://doi.org/10.51903/t881qw28
Wang, Q., Wang, Z., Li, C., Qiao, X., Guan, H., Zhou, Z., & Song, D. (2024). Research Progress in Corrosion Behavior and Anti-Corrosion Methods of Steel Rebar in Concrete. Metals, 14(8), 862. https://doi.org/10.3390/met14080862
Xu, Q., Liu, B., Dai, L., Yao, M., & Pang, X. (2024). Factors Influencing Chloride Ion Diffusion in Reinforced Concrete Structures. Materials, 17(13), 3296. https://doi.org/10.3390/ma17133296
Ye, C., Wu, H., Oguchi, T., Tang, Y., Pei, X., & Wu, Y. (2025). Physically Based and Data-Driven Models for Landslide Susceptibility Assessment: Principles, Applications, and Challenges. Remote Sensing, 17(13), 2280. https://doi.org/10.3390/rs17132280
Zhao, R., Li, C., & Guan, X. (2024). Advances in Modeling Surface Chloride Concentrations in Concrete Serving in the Marine Environment: A Mini Review. Buildings, 14(6), 1879. https://doi.org/10.3390/buildings14061879
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