Structural and load parameter estimation of a real‐world reinforced concrete slab bridge using measurements and Bayesian statistics. (3rd April 2022)

• Record Type: Journal Article
• Authors: Rózsás, Árpád; Slobbe, Arthur; Martini, Giulia; Jansen, Rob
• Is Part Of: Structural concrete. Volume 23:Number 6(2022)
• Page Start: 3569
• Page End: 3600
• Publication Date: 2022-04-03
• Subjects: Bayesian statistics -- finite element analysis -- parameter estimation -- real‐world concrete bridge -- static response measurements -- system identification; Reinforced concrete -- Periodicals; 624.1834
• DOI: 10.1002/suco.202100913
• Languages: English
• ISSNs: 1464-4177

Abstract

Abstract: This paper describes a static diagnostic load testing and measurement campaign of a reinforced concrete road bridge in Amsterdam. We consider 29 vertical translation sensors and 37 strain sensors. Multiple Bayesian parameter estimations are performed to estimate two structural and two load parameters of a three‐dimensional finite element (FE) model. The structural parameters are the concrete elastic modulus of the deck and the rotational spring stiffness at the piers. The load (truck) parameters are the load mass and position, which are estimated from separate measurements. We found that estimating (updating) the two selected structural parameters considerably improves the vertical translation model prediction accuracy over the FE model that was built before the measurement campaign, that is, the R 2 score increases from 0.90 to 0.98. Moreover, the load (truck) mass and position parameters can be estimated with high accuracy and high precision. When all measurements are used, the load mass and position are estimated with an error, respectively, less than 1.5 tonnes and 0.5 m with respect to the ground truth. On the negative side: when only certain strain measurements are used, the 90% posterior credible interval is about 3 m and 10 tonnes off the ground truth. Although the results are promising, they are restricted to the considered case and, more importantly, to operational loading conditions where the loading is well controlled. All data, models, and code are freely available upon request.