Development of Traffic Live-Load Models for Bridge Superstructure Rating with RBDO and Best Selection Approach

Siavashi, Sasan; Eamon, Christopher D.

doi:10.1061/(asce)be.1943-5592.0001457

Cited by 7 publications

(2 citation statements)

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“…In Oregon, truck WIM data were also used to develop state-specific LRFR live load factors, and the factors were calibrated using the same statistical methods that were used in the original development of LRFR [6]. Recently, Eamon et al conducted a reliability-based calibration of live load factors for bridge design specific to the state of Michigan, and it was found that Michigan load effects were greater than those previously assumed, requiring higher load factors than those in current use [7]; Rasheed et al implemented a structural reliability analysis of superstructure of highway bridges on the China-Pakistan Economic Corridor [8]; Oudah et al calibrated live load factors for bridge systems conveying extremely heavy mining trucks [9]; Anitori et al proposed a WIM-based live load model for advanced analysis of simply supported shortand medium-span highway bridges [10], and Siavashi and Eamon developed traffic live load models for bridge superstructure rating [11]. Besides, some efforts have been made to enhance the accuracy of load effect projections to longer period of time needed for design and rating [12][13][14][15][16][17].…”

Section: Introductionmentioning

confidence: 99%

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[Retracted] Calibration of the Live Load Factor for Highway Bridges with Different Requirements of Loading

Lang

Ren

Wang

2020

Advances in Civil Engineering

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Highway bridge load rating has been moving toward structural reliability since the issuance of AASHTO LRFR specifications; however, the recommended load factors were carried out by a few reliable truck data. The objective of this study is to calibrate the live load factor in AASHTO LRFR Rating Specification by using huge amount of WIM data collected in California for more than ten years between 2001 and 2013. Since traffic volumes, vehicular overloads, and traffic components are highly related to the load effect induced, a set of calibration equations is proposed here, in which the nominal standard load effect models are used and different requirements of loading are taken into account. By the analytical model of platoons of trucks and the extrapolation of the gathered WIM data over a short period of time to remote future over a longer time period, the expected maximum live load effects over the rating period of 5 years are also obtained. Then, the live load factor is calibrated as the product of the codified value multiplied by the ratio between the nominal standard load effect and the expected mean value. The results show that the products of the two ratios present rather constant, implying the proposed method and load configurations selected are effective. In the end, the live load factors of 1.0 and 0.7 along with load configurations are recommended for a simple span length less than 300 ft. The recommended calibration method and live load factors will eliminate the unnecessary overconservatism in rating specifications.

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Section: Introductionmentioning

confidence: 99%

“…Obviously, the recommended associated live load factors are less than 1.3, which is the reference live load factor c L,ref in equations (8) to (11). e fact indicates that the AASHTO train load is overconservative or unnecessary.…”

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confidence: 99%