Graph Neural Network Surrogate for Seismic Reliability Analysis of Highway Bridge Systems
Publication: Journal of Infrastructure Systems
Volume 30, Issue 4
Abstract
Rapid reliability assessment of transportation networks can enhance preparedness, risk mitigation, and response management procedures related to these systems. Network reliability analysis commonly considers network-level performance and does not consider the more detailed node-level responses due to computational cost. In this paper, we propose a rapid seismic reliability assessment approach for bridge networks based on graph neural networks, where node-level connectivities, between points of interest and other nodes, are evaluated under probabilistic seismic scenarios. Via numerical experiments on transportation systems in California, we demonstrate the accuracy, computational efficiency, and robustness of the proposed approach compared to the Monte Carlo approach.
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Data Availability Statement
Some data, models, or code that support the findings of this study, such as Python script, neural network model, and bridge data set, are available from the corresponding author upon reasonable request.
Acknowledgments
This material is based in part upon work supported by the National Science Foundation under Grant No. CMMI-1752302.
References
Adachi, T., and B. R. Ellingwood. 2009. “Serviceability assessment of a municipal water system under spatially correlated seismic intensities.” Comput.-Aided Civ. Infrastruct. Eng. 24 (4): 237–248. https://doi.org/10.1111/j.1467-8667.2008.00583.x.
Bommer, J. J., J. Douglas, F. Scherbaum, F. Cotton, H. Bungum, and D. Fah. 2010. “On the selection of ground-motion prediction equations for seismic hazard analysis.” Seismol. Res. Lett. 81 (5): 783–793. https://doi.org/10.1785/gssrl.81.5.783.
Chen, M., S. Mangalathu, and J.-S. Jeon. 2021. “Bridge fragilities to network fragilities in seismic scenarios: An integrated approach.” Eng. Struct. 237 (Dec): 112212. https://doi.org/10.1016/j.engstruct.2021.112212.
Chen, M., S. Mangalathu, and J.-S. Jeon. 2022. “Seismic reliability assessment of bridge networks considering travel time and connectivity reliabilities.” Earthquake Eng. Struct. Dyn. 51 (13): 3097–3110. https://doi.org/10.1002/eqe.3715.
Congress, S. S. C., J. Escamilla, H. Chimauriya, and A. J. Puppala. 2022. “Challenges of 360° inspection of bridge infrastructure using unmanned aerial vehicles (UAVs).” In Proc., Int. Conf. on Transportation and Development 2022, 96–108. Reston, VA: ASCE.
Cybersecurity and Infrastructure Security Agency. 2022. “Critical infrastructure sectors.” Accessed July 31, 2024. https://www.cisa.gov/critical-infrastructure-sectors.
Dong, Y., and D. M. Frangopol. 2017. “Probabilistic assessment of an interdependent healthcare–bridge network system under seismic hazard.” Struct. Infrastruct. Eng. 13 (1): 160–170. https://doi.org/10.1080/15732479.2016.1198399.
FEMA. 2022. “Hazus earthquake model technical manual.” Accessed July 31, 2024. https://www.fema.gov/flood-maps/tools-resources/flood-map-products/hazus/user-technical-manuals.
FHWA (Federal Highway Administration). 2018. “National bridge inventory.” US Department of Transportaiton. Accessed July 31, 2024. https://www.fhwa.dot.gov/bridge/nbi/ascii.cfm.
Google. 2022. “Google map.” Accessed July 31, 2024. https://www.google.com/maps.
Graizer, V., and E. Kalkan. 2016. “Summary of the GK15 ground-motion prediction equation for horizontal PGA and 5% damped PSA from shallow crustal continental earthquakes summary of the GK15 GMPE for horizontal PGA and 5% damped PSA.” Bull. Seismol. Soc. Am. 106 (2): 687–707. https://doi.org/10.1785/0120150194.
Hamilton, W. L., R. Ying, and J. Leskovec. 2017. “Inductive representation learning on large graphs.” In Proc., 31st Int. Conf. on Neural Information Processing Systems, 1025–1035. Red Hook, NY: Curran Associates Inc.
Karypis, G., and V. Kumar. 2022. MeTis: Unstructured graph partitioning and sparse matrix ordering system, version 4.0. Minneapolis: Univ. of Minnesota.
Kipf, T. N., and M. Welling. 2016. “Semi-supervised classification with graph convolutional networks.” Preprint, submitted September 9, 2016. https://arxiv.org/abs/1609.02907.
Lian, Q., P. Zhang, H. Li, W. Yuan, and X. Dang. 2021. “Adjustment method of bridge seismic importance factor based on bridge network connectivity reliability.” Structures 32 (Dec): 1692–1700. https://doi.org/10.1016/j.istruc.2021.03.113.
Liu, T., and H. Meidani. 2023a. “Optimizing seismic retrofit of bridges: Integrating efficient graph neural network surrogates and transportation equity.” In Proc., Cyber-Physical Systems and Internet of Things Week 2023, 367–372. New York: Association for Computing Machinery SIGBED.
Liu, T., and H. Meidani. 2023b. “Physics-informed neural networks for system identification of structural systems with a multiphysics damping model.” J. Eng. Mech. 149 (10): 04023079. https://doi.org/10.1061/JENMDT.EMENG-7060.
Nabian, M. A., and H. Meidani. 2018. “Deep learning for accelerated seismic reliability analysis of transportation networks.” Comput.-Aided Civ. Infrastruct. Eng. 33 (6): 443–458. https://doi.org/10.1111/mice.12359.
OpenStreetMap Contributors. 2017. “Planet dump.” Accessed July 31, 2024. https://www.openstreetmap.org.
Padgett, J., R. DesRoches, B. Nielson, M. Yashinsky, O.-S. Kwon, N. Burdette, and E. Tavera. 2008. “Bridge damage and repair costs from hurricane Katrina.” J. Bridge Eng. 13 (1): 6–14. https://doi.org/10.1061/(ASCE)1084-0702(2008)13:1(6).
Rangra, S., M. Sallak, W. Schön, and F. Vanderhaegen. 2015. “On the study of human reliability in transportation systems of systems.” In Proc., 2015 10th System of Systems Engineering Conf. (SoSE), 208–213. New York: IEEE.
Stewart, J. P., J. Douglas, M. Javanbarg, Y. Bozorgnia, N. A. Abrahamson, D. M. Boore, K. W. Campbell, E. Delavaud, M. Erdik, and P. J. Stafford. 2015. “Selection of ground motion prediction equations for the global earthquake model.” Earthquake Spectra 31 (1): 19–45. https://doi.org/10.1193/013013EQS017M.
Veličković, P., G. Cucurull, A. Casanova, A. Romero, P. Lio, and Y. Bengio. 2017. “Graph attention networks.” Preprint, submitted October 30, 2017. https://arxiv.org/abs/1710.10903.
Wan, C., Z. Yang, D. Zhang, X. Yan, and S. Fan. 2018. “Resilience in transportation systems: A systematic review and future directions.” Transport Rev. 38 (4): 479–498. https://doi.org/10.1080/01441647.2017.1383532.
Xu, H., Y. Ma, H.-C. Liu, D. Deb, H. Liu, J.-L. Tang, and A. K. Jain. 2020. “Adversarial attacks and defenses in images, graphs and text: A review.” Int. J. Autom. Comput. 17 (2): 151–178. https://doi.org/10.1007/s11633-019-1211-x.
Yoon, S., J. Kim, M. Kim, H.-Y. Tak, and Y.-J. Lee. 2020. “Accelerated system-level seismic risk assessment of bridge transportation networks through artificial neural network-based surrogate model.” Appl. Sci. 10 (18): 6476. https://doi.org/10.3390/app10186476.
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Published In
Journal of Infrastructure Systems
Volume 30 • Issue 4 • December 2024
Copyright
© 2024 American Society of Civil Engineers.
History
Received: Oct 13, 2022
Accepted: May 13, 2024
Published online: Aug 20, 2024
Published in print: Dec 1, 2024
Discussion open until: Jan 20, 2025
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