Technical Papers
Oct 7, 2024

Probabilistic Time–Variant Functionality-Based Analysis of Transportation Networks Incorporating Asphalt Pavements and Bridges under Multiple Hazards

Publication: Journal of Bridge Engineering
Volume 29, Issue 12

Abstract

Progressive and sudden deteriorations are the main reasons affecting the functionality of a transportation network. This paper presents a general probabilistic approach in which the ensembles of regression trees (ERT) are innovatively adopted to predict the life-cycle system reliability of asphalt pavements using Monte Carlo simulations, and pavement segments are considered with bridges in the analysis, prediction, and management of the functionality of transportation networks under both progressive and sudden deterioration due to multiple hazards. Four performance indicators of asphalt pavement subjected to multiple hazards were modeled using ERT trained with the Long-Term Pavement Performance database. The specific hazard types corresponding to each pavement performance indicator for the associated ERT model training were identified. The structural performance associated with bridge superstructures and substructures was analyzed by considering corrosion, traffic loading, and seismic hazards. The proposed approach is illustrated on an existing transportation network in Pennsylvania. The essential retrofitting timing, importance measure, and retrofitting priority associated with the individual component were investigated utilizing the calculated time-variant connectivity-based functionality and resilience associated with the network. The results demonstrate that asphalt pavements have a significant impact on the network functionality and should be considered in the postevent decision-making process of retrofitting strategies.

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Data Availability Statement

Some or all data, models, or codes that support the findings of this study are available from the corresponding author upon reasonable request.

Acknowledgments

The authors are grateful for the publicly accessible database provided by the LTPP program under the leadership of FHWA and the bridge information supplied by the Pennsylvania Department of Transportation (PennDOT).

References

AASHTO. 2017. AASHTO LRFD bridge design specifications. 8th ed. Washington, DC: AASHTO.
AASHTO. 2020. Mechanistic-empirical pavement design guide. 3rd ed. Washington, DC: AASHTO.
Akiyama, M., D. M. Frangopol, and H. Ishibashi. 2020. “Toward life-cycle reliability-, risk-and resilience-based design and assessment of bridges and bridge networks under independent and interacting hazards: Emphasis on earthquake, tsunami and corrosion.” Struct. Infrastruct. Eng. 16 (1): 26–50. https://doi.org/10.1080/15732479.2019.1604770.
Albrecht, P., and A. H. Naeemi. 1984. Performance of weathering steel in bridges (NCHRP report 272. Washington, DC: Transportation Research Board.
Ang, A. H. S., and W. H. Tang. 2007. Probability concepts in engineering planning and design. 2nd ed. New York: Wiley.
ASCE. 2021. 2021 report card for America’s infrastructure. Reston, VA: ASCE.
Baladi, G. Y., T. Dawson, G. Musunuru, M. Prohaska, and K. Thomas. 2017. Pavement performance measures and forecasting and the effects of maintenance and rehabilitation strategy on treatment effectiveness (No. FHWA-HRT-17-095). Washington, DC: Federal Highway Administration.
Biondini, F., E. Camnasio, and A. Titi. 2015. “Seismic resilience of concrete structures under corrosion.” Earthquake Eng. Struct. Dyn. 44 (14): 2445–2466. https://doi.org/10.1002/eqe.2591.
Breiman, L. 2001. “Random forests.” Mach. Learn. 45: 5–32. https://doi.org/10.1023/A:1010933404324.
Bruneau, M., et al. 2003. “A framework to quantitatively assess and enhance the seismic resilience of communities.” Earthquake Spectra 19 (4): 733–752. https://doi.org/10.1193/1.1623497.
Capacci, L., F. Biondini, and D. M. Frangopol. 2022. “Resilience of aging structures and infrastructure systems with emphasis on seismic resilience of bridges and road networks.” Resilient Cities Struct. 1 (2): 23–41. https://doi.org/10.1016/j.rcns.2022.05.001.
Chollet, F. 2021. Deep learning with python. New York: Simon and Schuster.
Dong, Y., and D. M. Frangopol. 2015. “Risk and resilience assessment of bridges under mainshock and aftershocks incorporating uncertainties.” Eng. Struct. 83: 198–208. https://doi.org/10.1016/j.engstruct.2014.10.050.
Elkins, G. E., and B. Ostrom. 2021. Long-term pavement performance information management system user guide (No. FHWA-HRT-21-038). McLean, VA: Federal Highway Administration.
Enright, M. P., and D. M. Frangopol. 1998. “Probabilistic analysis of resistance degradation of reinforced concrete bridge beams under corrosion.” Eng. Struct. 20 (11): 960–971. https://doi.org/10.1016/S0141-0296(97)00190-9.
Federal Highway Administration (FHWA)—Office of Research, Development, and Technology: Infrastructure R&D. 2017. The long-term pavement performance program (FHWA-HRT-15-049). McLean, VA: Federal Highway Administration.
Frangopol, D. M. 1985. “Sensitivity of reliability-based optimum design.” J. Struct. Eng. 111 (8): 1703–1721. https://doi.org/10.1061/(ASCE)0733-9445(1985)111:8(1703).
Frangopol, D. M. 1995. “Reliability-based optimum structural design.” In Chapter 16 Probabilistic structural mechanics handbook, edited by C. Sundararajan, 389–413. New York: Chapman & Hall/Springer.
Frangopol, D. M. 2011. “Life-cycle performance, management, and optimisation of structural systems under uncertainty: Accomplishments and challenges.” Struct. Infrastruct. Eng. 7 (6): 389–413. https://doi.org/10.1080/15732471003594427.
Frangopol, D. M., and P. Bocchini 2011. “Resilience as optimization criterion for the rehabilitation of bridges belonging to a transportation network subject to earthquake.” In Structures Congress 2011, 2044–2055. https://doi.org/10.1061/41171(401)178.
Frangopol, D. M., J. S. Kong, and E. S. Gharaibeh. 2001. “Reliability-based life-cycle management of highway bridges.” J. Comput. Civ. Eng. 15 (1): 27–34. https://doi.org/10.1061/(ASCE)0887-3801(2001)15:1(27).
Ghasemi, S. H., and J. Y. Lee. 2021a. “Measuring instantaneous resilience of a highway bridge subjected to earthquake events.” Transp. Res. Rec. 2675 (9): 1681–1692. https://doi.org/10.1177/03611981211009546.
Ghasemi, S. H., and J. Y. Lee. 2021b. “Reliability-based indicator for post-earthquake traffic flow capacity of a highway bridge.” Struct. Saf. 89: 102039. https://doi.org/10.1016/j.strusafe.2020.102039.
Ghosh, J., and J. E. Padgett. 2010. “Aging considerations in the development of time-dependent seismic fragility curves.” J. Struct. Eng. 136 (12): 1497–1511. https://doi.org/10.1061/(ASCE)ST.1943-541X.0000260.
Gollwitzer, S., and R. Rackwitz. 1983. “Equivalent components in first-order system reliability.” Reliab. Eng. 5 (2): 99–115. https://doi.org/10.1016/0143-8174(83)90024-0.
Gong, C., and W. Zhou. 2017. “Improvement of equivalent component approach for reliability analyses of series systems.” Struct. Saf. 68: 65–72. https://doi.org/10.1016/j.strusafe.2017.06.001.
Guo, R., D. Fu, and G. Sollazzo. 2022a. “An ensemble learning model for asphalt pavement performance prediction based on gradient boosting decision tree.” Int. J. Pavement Eng. 23 (10): 3633–3646. https://doi.org/10.1080/10298436.2021.1910825.
Guo, W., J. Zhang, D. Cao, and H. Yao. 2022b. “Cost-effective assessment of in-service asphalt pavement condition based on random forests and regression analysis.” Constr. Build. Mater. 330: 127219. https://doi.org/10.1016/j.conbuildmat.2022.127219.
Haas, R., and W. R. Hudson. 2015. Pavement asset management. Hoboken, NJ: John Wiley & Sons.
Han, X., and D. M. Frangopol. 2022. “Life-cycle connectivity-based maintenance strategy for bridge networks subjected to corrosion considering correlation of bridge resistances.” Struct. Infrastruct. Eng. 18 (12): 1614–1637. https://doi.org/10.1080/15732479.2021.2023590.
Han, X., and D. M. Frangopol. 2023. “Life-cycle risk-based optimal maintenance strategy for bridge networks subjected to corrosion and seismic hazards.” J. Bridge Eng. 28 (1): 04022128. https://doi.org/10.1061/JBENF2.BEENG-5799.
Hastie, T., R. Tibshirani, J. H. Friedman, and J. H. Friedman. 2009. The elements of statistical learning: Data mining, inference, and prediction. 2nd ed. New York: Springer.
Ishibashi, H., M. Akiyama, D. M. Frangopol, S. Koshimura, T. Kojima, and K. Nanami. 2020. “Framework for estimating the risk and resilience of road networks with bridges and embankments under both seismic and tsunami hazards.” Struct. Infrastruct. Eng. 17 (4): 494–514. https://doi.org/10.1080/15732479.2020.1843503.
James, G., D. Witten, T. Hastie, and R. Tibshirani. 2021. “Tree-based methods.” In An introduction to statistical learning, edited by G. Allen, R. De Veaux, and R. Nugent, 331–366. New York: Springer.
Kang, W. H., J. Song, and P. Gardoni. 2008. “Matrix-based system reliability method and applications to bridge networks.” Reliab. Eng. Syst. Saf. 93 (11): 1584–1593. https://doi.org/10.1016/j.ress.2008.02.011.
Limongelli, M. P., et al. 2024. “Bridge structural monitoring: The Lombardia regional guidelines.” Struct. Infrastruct. Eng. 20 (4): 461–484. https://doi.org/10.1080/15732479.2022.2107023.
Long-Term Pavement Performance (LTPP). 2023. Data from: LTPP DataPave online [dataset]. Accessed September 2023. https://infopave.fhwa.dot.gov/Data/StandardDataRelease
Miner, M. A. 1945. “Cumulative damage in fatigue.” J. Appl. Mech. 12 (3): A159–A164. https://doi.org/10.1115/1.4009458.
Padgett, J. E. 2007. Seismic vulnerability assessment of retrofitted bridges using probabilistic methods. Atlanta: Georgia Institute of Technology.
PennDOT (Pennsylvania Department of Transportation). 2023 “Traffic Information Repository (TIRe).” Accessed September 6, 2023. https://gis.penndot.gov/tire.
Pescaroli, G., R. T. Wicks, G. Giacomello, and D. E. Alexander. 2018. “Increasing resilience to cascading events: The M. OR. D. OR. scenario.” Saf. Sci. 110: 131–140. https://doi.org/10.1016/j.ssci.2017.12.012.
Selezneva, O. I., Y. J. Jiang, and G. Mladenovic. 2002. Evaluation and analysis of LTPP pavement layer thickness data (No. FHWA-RD-03-041). Washington, DC: Federal Highway Administration.
Stewart, M. G. 2004. “Spatial variability of pitting corrosion and its influence on structural fragility and reliability of RC beams in flexure.” Struct. Saf. 26 (4): 453–470. https://doi.org/10.1016/j.strusafe.2004.03.002.
USGS. 2023. “Unified hazard tool.” Accessed May 2023. https://earthquake.usgs.gov/nshmp-haz-ws/apps/services.html.
Wu, J., X. Y. Chen, H. Zhang, L. D. Xiong, H. Lei, and S. H. Deng. 2019. “Hyperparameter optimization for machine learning models based on Bayesian optimization.” J. Electron. Sci. Technol. 17 (1): 26–40.
Xin, J., M. Akiyama, and D. M. Frangopol. 2023a. “Sustainability-informed management optimization of asphalt pavement considering risk evaluated by multiple performance indicators using deep neural networks.” Reliab. Eng. Syst. Saf. 238: 109448. https://doi.org/10.1016/j.ress.2023.109448.
Xin, J., M. Akiyama, D. M. Frangopol, and M. Zhang. 2022. “Multi-objective optimisation of in-service asphalt pavement maintenance schedule considering system reliability estimated via LSTM neural networks.” Struct. Infrastruct. Eng. 18 (7): 1002–1019. https://doi.org/10.1080/15732479.2022.2038641.
Xin, J., M. Akiyama, D. M. Frangopol, M. Zhang, J. Pei, and J. Zhang. 2021. “Reliability-based life-cycle cost design of asphalt pavement using artificial neural networks.” Struct. Infrastruct. Eng. 17 (6): 872–886. https://doi.org/10.1080/15732479.2020.1815807.
Xin, J., D. M. Frangopol, M. Akiyama, and X. Han. 2023b. “Probabilistic life-cycle connectivity assessment of transportation networks using deep learning.” J. Bridge Eng. 28 (9): 04023066. https://doi.org/10.1061/JBENF2.BEENG-6149.
Yilmaz, T., S. Banerjee, and P. A. Johnson. 2016. “Performance of two real-life California bridges under regional natural hazards.” J. Bridge Eng. 21 (3): 04015063. https://doi.org/10.1061/(ASCE)BE.1943-5592.0000827.

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Go to Journal of Bridge Engineering
Journal of Bridge Engineering
Volume 29Issue 12December 2024

History

Received: Oct 12, 2023
Accepted: Aug 12, 2024
Published online: Oct 7, 2024
Published in print: Dec 1, 2024
Discussion open until: Mar 7, 2025

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Authors

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Associate Research Fellow, Dept. of Bridge and Tunnel Engineering, School of Transportation Science and Engineering, Harbin Institute of Technology, Harbin 150090, China; formerly, Postdoctoral Research Associate, Dept. of Civil and Environmental Engineering, ATLSS Engineering Research Center, Lehigh Univ., Bethlehem, PA 18015-4729. ORCID: https://orcid.org/0000-0001-9812-8130. Email: [email protected]
Professor and the Fazlur R. Khan Endowed Chair of Structural Engineering and Architecture, Dept. of Civil and Environmental Engineering, ATLSS Engineering Research Center, Lehigh Univ., Bethlehem, PA 18015-4729 (corresponding author). ORCID: https://orcid.org/0000-0002-9213-0683. Email: [email protected]
Professor, Dept. of Civil and Environmental Engineering, Waseda Univ., Tokyo 169-8555, Japan. ORCID: https://orcid.org/0000-0001-9560-2159. Email: [email protected]

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