Cognitive Approaches to Hyperbolic Discounting of High-Impact Low-Probability Bridge Overload Events and Live-Load Factors
Publication: Journal of Performance of Constructed Facilities
Volume 36, Issue 2
Abstract
Temporal discounting is a cognitive behavior with important implications in preparing for high-impact and low-probability events. Evaluation of live-load effects has a great influence on the design, maintenance, and rehabilitation of bridges in the US. This study employed statistical methods to evaluate high-impact and low-probability bridge overloading events and proposes a cognitive approach to evaluating live-load factors because stakeholders discount the probability of observing overweight vehicles based on Kahneman and Tversky’s prospect theory. This paper quantified the likelihood of observing extreme weights on bridges from 10 Weigh-In-Motion sites in Georgia using the extreme value theory. A sensitivity analysis showed how predicted live-load factors vary in response to the choice of a threshold or an extreme percentile. Subsequently, the process of predicting maximum live-load factors was validated using another state’s data. It was concluded that a live-load factor is affected by a shape parameter and is numerically quantifiable for each site, and that near-term live-load factors are more salient for preparing bridges for high-risk, low-probability overloading events.
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Data Availability Statement
Some or all data, models, or code that support the findings of this study are available from the corresponding author upon reasonable request.
Acknowledgments
The study presented in this paper was conducted by the University of Georgia under the auspices of the Georgia Department of Transportation (GDOT n.d.) (RP 18-36). The opinions, findings, and conclusions may not reflect the views of the funding agency or other individuals.
References
AASHTO. 2017. LRFD bridge design specifications. Washington, DC: AASHTO.
AASHTO. 2018. The manual for bridge evaluation. Washington, DC: AASHTO.
Blanchard, T. C., J. M. Pearson, and B. Y. Hayden. 2013. “Postreward delays and systematic biases in measures of animal temporal discounting.” Proc. Natl. Acad. Sci. U.S.A. 110 (38): 15491–15496. https://doi.org/10.1073/pnas.1310446110.
Caprani, C. C., and E. J. OBrien. 2010. “The use of predictive likelihood to estimate the distribution of extreme bridge traffic load effect.” Struct. Saf. 32 (2): 138–144. https://doi.org/10.1016/j.strusafe.2009.09.001.
Chen, W., C. Ma, Z. Xie, B. Yan, and J. Xu. 2015. “Improvement of extrapolation of traffic load effect on highway bridges based on Rice’s theory.” Int. J. Steel Struct. 15 (3): 527–539. https://doi.org/10.1007/s13296-015-9002-x.
Davison, A. C., and R. L. Smith. 1990. “Models for exceedances over high thresholds.” J. R. Stat. Soc. Series B 52 (3): 393–425. https://doi.org/10.1073/pnas.1310446110.
Enright, B., and E. J. O’Brien. 2013. “Monte Carlo simulation of extreme traffic loading on short and medium span bridges.” Struct. Infrastruct. Eng. 9 (12): 1267–1282. https://doi.org/10.1080/15732479.2012.688753.
Fu, G., and J. You. 2011. “Extrapolation for future maximum load statistics.” J. Bridge Eng. 16 (4): 527–535. https://doi.org/10.1061/(ASCE)BE.1943-5592.0000175.
GDOT. n.d. Georgia’s traffic monitoring program. Atlanta: Office of Transportation Data.
Ghosn, M., and D. M. Frangopol. 1996. “Site-specific live load models for bridge evaluation.” In Probabilistic mechanics & structural reliability, 30–33. Reston, VA: ASCE.
INDOT (Indiana DOT). 2020. “Indiana Department of Transportation.” Accessed June 7, 2020. https://www.in.gov/indot/resources/traffic-data/.
James, G. 2003. Analysis of traffic load effects on railway bridges. Stockholm, Sweden: Byggvetenskap.
Kahneman, D., and A. Tversky. 1979. “On the interpretation of intuitive probability: A reply to Jonathan Cohen.” Cognition 7 (4): 409–411. https://doi.org/10.1016/0010-0277(79)90024-6.
Keeney, R. L., and T. L. McDaniels. 1992. “Value-focused thinking about strategic decisions at BC Hydro.” Interfaces 22 (6): 94–109. https://doi.org/10.1287/inte.22.6.94.
Moses, F. 2001. Calibration of load factors for LRFR bridge evaluation. Washington, DC: National Academy Press.
Nowak, A. S. 1999. Calibration of LRFD bridge design code. Washington, DC: Transportation Research Board.
Nowak, A. S., and P. Rakoczy. 2013. “WIM-based live load for bridges.” KSCE J. Civ. Eng. 17 (3): 568–574. https://doi.org/10.1007/s12205-013-0602-8.
Pidgeon, N., and C. Butler. 2009. “Risk analysis and climate change.” Environ. Polit. 18 (5): 670–688. https://doi.org/10.1080/09644010903156976.
Soriano, M., J. R. Casas, and M. Ghosn. 2017. “Simplified probabilistic model for maximum traffic load from weigh-in-motion data.” Struct. Infrastruct. Eng. 13 (4): 454–467. https://doi.org/10.1080/15732479.2016.1164728.
Zhou, X.-Y., F. Schmidt, F. Toutlemonde, and B. Jacob. 2016. “A mixture peaks over threshold approach for predicting extreme bridge traffic load effects.” Probab. Eng. Mech. 43 (Jan): 121–131. https://doi.org/10.1016/j.probengmech.2015.12.004.
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Published In
Journal of Performance of Constructed Facilities
Volume 36 • Issue 2 • April 2022
Copyright
© 2022 American Society of Civil Engineers.
History
Received: Mar 25, 2021
Accepted: Dec 2, 2021
Published online: Feb 3, 2022
Published in print: Apr 1, 2022
Discussion open until: Jul 3, 2022
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