Using NDT Data for Finite Element Model Calibration and Load Rating of Bridges

Yost, Joseph Robert; Schulz, J L; Commander, Brett C.

doi:10.1061/40753(171)3

Cited by 18 publications

(2 citation statements)

References 1 publication

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“…As a result, a bridge may require signage postings to limit allowable truck loads. Load ratings using baseline FEMs may significantly reduce the amount of subjectivity present in conventional load ratings (Yost et al 2005).…”

Section: Load Ratingmentioning

confidence: 99%

Instrumentation, Nondestructive Testing, and Finite-Element Model Updating for Bridge Evaluation Using Strain Measurements

et al. 2012

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A baseline finite element model was developed for bridge management and calibration using nondestructive test data. The model calibration technique was evaluated on the Vernon Avenue Bridge over the Ware River in Barre, Massachusetts. This newly constructed bridge was instrumented throughout its construction phases in preparation for a static truck load test performed before the bridge opening. The strain data collected during the load test was used to calibrate a detailed baseline finite element model in an effort to represent the 3D system behavior of the bridge. Three methods of load ratings were used and compared: (1) conventional method, (2) conventional method updated by using NDT data, and (3) finite element model calibrated with NDT data.

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Section: Load Ratingmentioning

confidence: 99%

Instrumentation, Nondestructive Testing, and Finite-Element Model Updating for Bridge Evaluation Using Strain Measurements

et al. 2012

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“…They also found that the calibrated FE model generally resulted in higher overall load rating factors than those using conventional approaches. Other research studies on improving load rating results through conducting nondestructive bridge testing and establishing and updating FE models were reported by Chajes and Shenton [7], Yost et al [8], and Schlune et al [9].…”

Section: Introductionmentioning

confidence: 99%

Automated bridge load rating determination utilizing strain response due to ambient traffic trucks

Deng

Phares

2016

Engineering Structures

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Approximately 11% of bridges in the United States are categorized as structurally deficient and there is a marked need of more accurately evaluating true structural capacity. Structural Health Monitoring (SHM) systems can provide a timely indication of the need for maintenance, repair, rehabilitation and replacement of bridges and can greatly improve the apportionment and management of limited resources. This paper presents an Automated Ambient Traffic (AAT) approach for determining load rating of bridges monitored by the BECAS SHM system under ambient traffic. The AAT approach was developed through a process integration of truck detection, bridge model calibration, and bridge load rating: (1) The quasi-static bridge strain response and the characteristics of associated trucks are collected; (2) Multiple trucks are randomly sampled from a historic Weigh-In-Motion (WIM) database; and (3) For each combination of strain response and truck selection, an Finite Element (FE) model is calibrated and used to calculate a load rating. Sampling strategies were discussed for appropriately quantifying the influence of uncertainties of truck characteristics on the calibration and load rating results. To demonstrate this approach, a sample three-span, five-girder, and two-lane steel girder/concrete deck (I-80) bridge was utilized. A load rating of the I-80 Bridge using the Traditional Known Truck (TKT) approach was performed to provide benchmark results. The results of the calibration and load rating using the AAT approach were derived using three different sampling strategies and compared to those using the TKT approach. The sampling strategy, selecting strain response with a spectrum of higher peak girder strains, associated trucks with a spectrum of higher gross vehicle weights, and two truck events on south and north

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Experimental verification for load rating of steel truss bridge using an improved Hamiltonian Monte Carlo-based Bayesian model updating

Baisthakur

Chakraborty

2021

J Civil Struct Health Monit

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Using NDT Data for Finite Element Model Calibration and Load Rating of Bridges

Cited by 18 publications

References 1 publication

Instrumentation, Nondestructive Testing, and Finite-Element Model Updating for Bridge Evaluation Using Strain Measurements

Instrumentation, Nondestructive Testing, and Finite-Element Model Updating for Bridge Evaluation Using Strain Measurements

Automated bridge load rating determination utilizing strain response due to ambient traffic trucks

Experimental verification for load rating of steel truss bridge using an improved Hamiltonian Monte Carlo-based Bayesian model updating

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