Deep Learning–Based Retrofitting and Seismic Risk Assessment of Road Networks
Publication: Journal of Computing in Civil Engineering
Volume 36, Issue 2
Abstract
Seismic risk assessment of road systems involves computationally expensive traffic simulations to evaluate the performance of the system. To accelerate this process, this paper develops a neural network surrogate model that allows rapid and accurate estimation of changes in traffic performance metrics due to bridge damage. Some of the methodological aspects explored when calibrating this neural network are defining sampling protocols, selecting hyperparameters, and evaluating practical considerations of the model. In addition to the neural network, a modified version of the local interpretable model-agnostic explanation (LIME) is proposed as a retrofitting strategy that minimizes earthquakes’ impact on the system. The modified version (LIME-TI) uses traffic impacts (TI) and rates of occurrence to aggregate the importance of individual damage realizations during the computation of variable importance. This study uses the San Francisco Bay Area road network as a testbed. As a conclusion of this study, the neural network accurately predicts the system’s performance while taking five orders of magnitude less time to compute traffic metrics, allowing decision-makers to evaluate the impact of retrofitting bridges in the system quickly. Moreover, the proposed LIME-TI metric is superior to others (such as traffic volume or vulnerability) in identifying bridges whose retrofit effectively improves network performance.
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Data Availability Statement
Some data, models, or code generated or used during the study are available in a repository online in accordance with funder data retention policies, which can be found in: DOI 10.5281/zenodo.5161259.
Acknowledgments
The State of California supported this work through the Transportation System Research Program of the Pacific Earthquake Engineering Research Center (PEER) and by the Shah Family Fund Fellowship. Some of the computing for this project was performed on the Sherlock cluster. The authors would like to thank Stanford University and the Stanford Research Computing Center for providing computational resources and support that contributed to these research results. Any opinions, findings, conclusions, or recommendations expressed in this material are those of the authors and do not necessarily reflect those of the funding agency.
References
Alemzadeh, S., H. Talebiyan, S. Talebi, L. Duenas-Osorio, and M. Mesbahi. 2020. “Deep learning-based resource allocation for infrastructure resilience.” Preprint, submitted July 12, 2016. https://arxiv.org/abs/2007.05880.
Basoz, N., and A. S. Kiremidjian. 1995. Prioritization of bridges for seismic retrofitting. Buffalo, NY: National Center for Earthquake Engineering Research.
Beckmann, M., C. B. McGuire, and C. B. Winsten. 1956. Studies in the economics of transportation. New Haven, CT: Yale University Press.
Bommer, J., S. Scott, and S. Sarma. 2000. “Hazard-consistent earthquake scenarios.” Soil Dyn. Earthquake Eng. 19 (4): 219–231. https://doi.org/10.1016/S0267-7261(00)00012-9.
Bommer, J., R. Spence, M. Erdik, S. Tabuchi, N. Aydinoglu, E. Booth, D. Del Re, and O. Peterken. 2002. “Development of an earthquake loss model for turkish catastrophe insurance.” J. Seismolog. 6 (3): 431–446. https://doi.org/10.1023/A:1020095711419.
Boore, D. M., and G. M. Atkinson. 2008. “Ground-motion prediction equations for the average horizontal component of pga, pgv, and 5%-damped psa at spectral periods between 0.01 s and 10.0 s.” Earthquake Spectra 24 (1): 99–138. https://doi.org/10.1193/1.2830434.
Caltrans. 2019. Caltrans seismic design criteria. Sacramento, CA: California DOT.
Chen, M., and A. S. Alfa. 1991. “A network design algorithm using a stochastic incremental traffic assignment approach.” Transp. Sci. 25 (3): 215–224. https://doi.org/10.1287/trsc.25.3.215.
Elhag, T. M., and Y.-M. Wang. 2007. “Risk assessment for bridge maintenance projects: Neural networks versus regression techniques.” J. Comput. Civ. Eng. 21 (6): 402–409. https://doi.org/10.1061/(ASCE)0887-3801(2007)21:6(402).
FEMA. 2003. Multi-hazard loss estimation methodology: Earthquake model, 235–260. Washington, DC: FEMA.
Field, E. H., et al. 2009. “Uniform california earthquake rupture forecast, version 2 (ucerf 2).” Bull. Seismol. Soc. Am. 99 (4): 2053–2107. https://doi.org/10.1785/0120080049.
Field, E. H., T. H. Jordan, and C. A. Cornell. 2003. “Opensha: A developing community-modeling environment for seismic hazard analysis.” Seismol. Res. Lett. 74 (4): 406–419. https://doi.org/10.1785/gssrl.74.4.406.
Gomez, C., and J. W. Baker. 2019. “An optimization-based decision support framework for coupled pre-and post-earthquake infrastructure risk management.” Struct. Saf. 77: 1–9. https://doi.org/10.1016/j.strusafe.2018.10.002.
Gomez, C., M. Sanchez-Silva, L. Dueñas-Osorio, and D. Rosowsky. 2013. “Hierarchical infrastructure network representation methods for risk-based decision-making.” Struct. Infrastruct. Eng. 9 (3): 260–274.
Han, Y., and R. A. Davidson. 2012. “Probabilistic seismic hazard analysis for spatially distributed infrastructure.” Earthquake Eng. Struct. Dyn. 41 (15): 2141–2158.
Jayaram, N., and J. W. Baker. 2009. “Correlation model for spatially distributed ground-motion intensities.” Earthquake Eng. Struct. Dyn. 38 (15): 1687–1708. https://doi.org/10.1002/eqe.922.
Kingma, D. P., and J. Ba. 2014. “Adam: A method for stochastic optimization.” Preprint, submitted December 22, 2014. https://arxiv.org/abs/1412.6980.
Lim, H.-W., J. Song, and N. Kurtz. 2015. “Seismic reliability assessment of lifeline networks using clustering-based multi-scale approach.” Earthquake Eng. Struct. Dyn. 44 (3): 355–369. https://doi.org/10.1002/eqe.2472.
Liu, C., Y. Fan, and F. Ordóñez. 2009. “A two-stage stochastic programming model for transportation network protection.” Comput. Oper. Res. 36 (5): 1582–1590. https://doi.org/10.1016/j.cor.2008.03.001.
Lundberg, S., and S.-I. Lee. 2017. “A unified approach to interpreting model predictions.” Preprint, submitted May 22, 2017. https://arxiv.org/abs/1705.07874.
Miller, M. 2014. “Seismic risk assessment of complex transportation networks.” Ph.D. thesis, Dept. of Civil and Environmental Engineering, Stanford Univ.
Miller, M., and J. Baker. 2015. “Ground-motion intensity and damage map selection for probabilistic infrastructure network risk assessment using optimization.” Earthquake Eng. Struct. Dyn. 44 (7): 1139–1156. https://doi.org/10.1002/eqe.2506.
Miller-Hooks, E., X. Zhang, and R. Faturechi. 2012. “Measuring and maximizing resilience of freight transportation networks.” Comput. Oper. Res. 39 (7): 1633–1643. https://doi.org/10.1016/j.cor.2011.09.017.
Nabian, M. A., and H. Meidani. 2018a. “Accelerating stochastic assessment of post-earthquake transportation network connectivity via machine-learning-based surrogates.” In Proc., Transportation Research Board 97th Annual Meeting. Washington, DC: Transportation Research Board.
Nabian, M. A., and H. Meidani. 2018b. “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.
Olden, J. D., M. K. Joy, and R. G. Death. 2004. “An accurate comparison of methods for quantifying variable importance in artificial neural networks using simulated data.” Ecol. Modell. 178 (3–4): 389–397. https://doi.org/10.1016/j.ecolmodel.2004.03.013.
Özdamar, L., and O. Demir. 2012. “A hierarchical clustering and routing procedure for large scale disaster relief logistics planning.” Transp. Res. Part E Logist. Transp. Rev. 48 (3): 591–602. https://doi.org/10.1016/j.tre.2011.11.003.
Ribeiro, M. T., S. Singh, and C. Guestrin. 2016. “Why should I trust you?’: Explaining the predictions of any classifier.” In Proc., 22nd ACM SIGKDD Int. Conf. on Knowledge Discovery and Data Mining, 1135–1144. New York: Association for Computing Machinery.
Rokneddin, K., J. Ghosh, L. Dueñas-Osorio, and J. E. Padgett. 2013. “Bridge retrofit prioritisation for ageing transportation networks subject to seismic hazards.” Struct. Infrastruct. Eng. 9 (10): 1050–1066. https://doi.org/10.1080/15732479.2011.654230.
Tomar, A., and H. V. Burton. 2021. “Active learning method for risk assessment of distributed infrastructure systems.” Comput.-Aided Civ. Infrastruct. Eng. 36 (4): 438–452. https://doi.org/10.1111/mice.12665.
Wald, D. J., and T. I. Allen. 2007. “Topographic slope as a proxy for seismic site conditions and amplification.” Bull. Seismol. Soc. Am. 97 (5): 1379–1395. https://doi.org/10.1785/0120060267.
Werner, S. D., C. E. Taylor, S. Cho, J.-P. Lavoie, C. K. Huyck, C. Eitzel, H. Chung, and R. T. Eguchi. 2006. Redars 2 methodology and software for seismic risk analysis of highway systems. Buffalo, NY: National Center for Earthquake Engineering Research.
Xie, Y., M. Ebad Sichani, J. E. Padgett, and R. DesRoches. 2020. “The promise of implementing machine learning in earthquake engineering: A state-of-the-art review.” Earthquake Spectra 36 (4): 1769–1801. https://doi.org/10.1177/8755293020919419.
Yu, H., Z. Wu, S. Wang, Y. Wang, and X. Ma. 2017. “Spatiotemporal recurrent convolutional networks for traffic prediction in transportation networks.” Sensors 17 (7): 1501. https://doi.org/10.3390/s17071501.
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Published In
Journal of Computing in Civil Engineering
Volume 36 • Issue 2 • March 2022
Copyright
© 2021 American Society of Civil Engineers.
History
Received: Aug 5, 2021
Accepted: Oct 15, 2021
Published online: Dec 15, 2021
Published in print: Mar 1, 2022
Discussion open until: May 15, 2022
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