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Structure and Infrastructure Engineering
Maintenance, Management, Life-Cycle Design and Performance
Volume 19, 2023 - Issue 10
358
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Articles

Fatigue reliability assessment and life-cycle cost analysis of roadway bridges equipped with weigh-in-motion systems

, ORCID Icon, , , &
Pages 1317-1333 | Received 09 Jun 2021, Accepted 08 Oct 2021, Published online: 05 Jan 2022
 

Abstract

The fatigue safety assessment of critical details in steel bridges is dominated by several uncertainties mainly related to load modelling. The use of integrated weigh-in-motion (WIM) systems in roadway bridges allows to collect all the necessary information for tracking actual traffic conditions. Based on WIM data, the uncertainty associated to the evaluation of the probability of fatigue failure is reduced, allowing the optimal scheduling of repair and maintenance activities and thus improving safety and economic savings. The paper presents a methodology for estimating fatigue life cycle cost (LCC) of roadway bridges equipped with a WIM system that considers uncertainties in measurement and modelling errors. The procedure allows the quantification of the economic gain enabled by WIM systems, useful in assessing the cost/accuracy trade-off for a given WIM system. It is found that the continuous use of a WIM system may improve the rate of decrease of the structural reliability index, compared with temporally limited use of WIM data. The periodic update of the reliability index and of the probability of failure based on WIM measurements results in a rather accurate estimation of the fatigue life cycle cost, which is crucial in identifying the payback time of the WIM system.

Disclosure statement

No potential conflict of interest was reported by the authors.

Additional information

Funding

The authors gratefully acknowledge the financial support of the “Cassa di Risparmio di Perugia” Foundation, that funded this study through the Project “Development of an original life-cycle cost model for the optimal management of bridges and viaducts equipped with SHM systems” (Grant number 2019.0338.029), and the support of Brent Phares at Iowa State University and the Iowa Department of Transportation that provided the field monitoring data.

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