Structural and load parameter estimation of a real‐world reinforced concrete slab bridge using measurements and Bayesian statistics

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.

Efficient Bayesian model selection and calibration using field data for a reinforced concrete slab bridge

“…Moreover, also strain sensors in the transverse direction are located in these spans to provide information on the transverse load distribution. Information on the characteristics and accuracy of the sensors can be found in (Rozsas et al, 2022).…”

“…Test series T1,all + T2,1 is considered only for evaluating the prediction accuracy of the calibrated model (section 5). Additional information about the measurement campaign, the sensors, and the processing of the measurement data can be found in (Rozsas et al, 2022). It should be pointed out that the datapoints considered in the current work correspond to those of one load test and are no continuous measurements over a longer timespan.…”

“…The only modelled load is the truck weight. Additional information about the FE model can be found in (Rozsas et al, 2022).…”

“…• Case 1: The fixed (deterministic) standard deviation σpred of the prediction errors is assumed equal to 1 µε for the strains and 0.05 mm for the displacements (Rozsas et al, 2022). Hence, the covariance matrix for the prediction error is a diagonal matrix with its diagonal elements equal to σpred².…”

A Bayesian Framework for Simulation‐based Digital Twins of Bridges

“…In cases where the structural model presents a heterogeneous material behaviour, Bayesian inference methods are used to obtain the parameters of the representation of the material variability 14 . For the case of bridges, SHM systems are calibrated following this methodology 15 , bridge model parameters are obtained using data from measurement campaigns 16 and digital twins are tuned to represent the response of a bridge in real time 17 .…”