Stochastic Analysis of Network-Level Bridge Maintenance Needs Using Latin Hypercube Sampling
Publication: ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part A: Civil Engineering
Volume 7, Issue 1
Abstract
The deterioration of bridge infrastructure along with diminishing funding resources necessitates reliable planning for future budget needs to maintain bridges at an acceptable level of performance. Although existing methodologies do consider uncertainties in individual bridge deterioration, there is a gap in the development of a network-level budget planning framework incorporating such stochastic phenomena. In this paper, a network-level needs-prediction simulation framework is proposed, that constructs confidence intervals for the output within a specific precision and significance level. Additionally, as the vast network size sets a demand for high computational effort, the Latin hypercube sampling technique is introduced to reduce the inherent simulation variance and decrease the number of replications needed. Ultimately, the applicability of the proposed methodology is demonstrated using a case study pertaining to a network of structures comprising bridges and culverts within the Austin District of the Texas DOT (TxDOT). The results confirm the capability of the proposed methodology in providing meaningful budget confidence interval estimates at the network level by using a significantly reduced number of computational resources.
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Data Availability Statement
Some or all data, models, or code generated or used during the study are proprietary or confidential in nature and may only be provided with restrictions. Part of the case study was developed using the proprietary software Palisade Evolver along with Microsoft Excel. There are no other applicable restrictions.
Acknowledgments
Part of this research was supported by the Texas DOT Austin District. The authors wish to express their gratitude to Dr. Hui Wu, P.E., and Ms. Brenda Guerra, P.E., for providing bridge inspection data that was used in the case study.
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Published In
ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part A: Civil Engineering
Volume 7 • Issue 1 • March 2021
Copyright
© 2020 American Society of Civil Engineers.
History
Received: Mar 31, 2020
Accepted: Aug 3, 2020
Published online: Oct 31, 2020
Published in print: Mar 1, 2021
Discussion open until: Mar 31, 2021
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