Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

Bridge Digital Twining Using an Output-Only Bayesian Model Updating Method and Recorded Seismic Measurements

Version 1 : Received: 21 December 2021 / Approved: 23 December 2021 / Online: 23 December 2021 (11:57:54 CET)

How to cite: Ghahari, F.; Malekghaini, N.; Ebrahimian, H.; Taciroglu, E. Bridge Digital Twining Using an Output-Only Bayesian Model Updating Method and Recorded Seismic Measurements. Preprints 2021, 2021120389 (doi: 10.20944/preprints202112.0389.v1). Ghahari, F.; Malekghaini, N.; Ebrahimian, H.; Taciroglu, E. Bridge Digital Twining Using an Output-Only Bayesian Model Updating Method and Recorded Seismic Measurements. Preprints 2021, 2021120389 (doi: 10.20944/preprints202112.0389.v1).

Abstract

Rapid post-earthquake damage diagnosis of bridges can guide decision-making for emergency response management and recovery. This can be facilitated using digital technologies to remove the barriers of manual post-event inspections. Prior mechanics-based Finite Element (FE) models can be used for post-event response simulation using the measured ground motions at nearby stations; however, the damage assessment outcomes would suffer from uncertainties in structural and soil material properties, input excitations, etc. For instrumented bridges, these uncertainties can be reduced by integrating sensory data with prior models through a model updating approach. This study presents a sequential Bayesian model updating technique, through which a linear/nonlinear FE model, including soil-structure interaction effects, and the foundation input motions are jointly identified from measured acceleration responses. The efficacy of the presented model updating technique is first examined through a numerical verification study. Then, seismic data recorded from the San Rogue Canyon Bridge in California are used for a real-world case study. Comparison between the free-field and the foundation input motions reveals valuable information regarding the soil-structure interaction effects at the bridge site. Moreover, the reasonable agreement between the recorded and estimated bridge responses shows the potentials of the presented model updating technique for real-world applications. The updated FE model is considered as the digital twin of the bridge and can be used to analyze the bridge and monitor the structural response at element, section, and fiber levels to diagnose the location and severity of any potential damage mechanism.

Keywords

Structural health monitoring, Digital twin, Damage diagnosis, Finite element model updating, Bayesian inference, Soil-structure interaction, Foundation input motion, Rapid post-earthquake assessment

Subject

ENGINEERING, Civil Engineering

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