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

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 described process is a practice of digital twinning and 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.

Bridge management through digital twin-based anomaly detection systems: A systematic review

“…Ramancha et al (2020) implement an advanced Bayesian inference approach using Sequential Monte Carlo (SMC) simulations to update the material and damping model parameters of a full-scale reinforced-concrete column under dynamic loading based on the heterogeneous data collected by accelerometers, strain gauges, GPS displacements, and potentiometers. Similarly, by applying a Bayesian inference approach, Ghahari et al (2022) successfully update an FE model including soil-structure interaction effects. This was possible thanks to the motion identification at foundation level based on the acceleration measurement data obtained from the BHM.…”

Research progress and prospect of digital twin in bridge engineering

“…Journal of civil structural health monitoring O&M (Dang and Shim, 2020) 14 Lecture notes in civil engineering O&M (Bello et al, 2022) 3 Lecture notes in civil engineering F, O&M (Zhou et al, 2022) -Lecture notes in civil engineering F, O&M (Kang et al, 2021) 27 Multimedia tools and applications F, O&M (Mohammadi et al, 2021) 37 Remote sensing E (Marra et al, 2021) 3 SCIRES-IT E (Shao et al, 2020) 19 Sensors E (Ghahari et al, 2022) 6 Sensors O&M (Yu et al, 2022) 5 Structural control and health monitoring O&M (Yoon et al, 2022) 2 Structural and multidisciplinary optimization O&M (Ye et al, 2019) 40 Proceedings of the 12th international workshop on structural health monitoring O&M (Shu et al, 2019) 3 Proceedings of the 12th international workshop on structural health monitoring E, O&M (Jiang et al, 2021a) 4 Structural monitoring and maintenance O&M (Shim et al, 2019a) 99 Structure and infrastructure engineering F, O&M (Omer et al, 2019) 45 Structure and infrastructure engineering E, O&M (Ye et al, 2020a) 7 Structure and infrastructure engineering O&M (Jiang et al, 2022a) 1 Structure and infrastructure engineering F, O&M (Futai et al, 2022) 3 Structure and infrastructure engineering F, O&M Note: The number of citations in the table is based on the Scopus data as of June 2023. The meaning of the symbols in the table is defined in the following section.…”

“…Applications References Perception Virtual inspection (Marra et al, 2021;Omer et al, 2019Omer et al, , 2021) Real-time monitoring (Dang et al, 2022;Zhou et al, 2022;Ye et al, 2019;Shao et al, 2020;Lai et al, 2022) Evaluation Analysis diagnosis (Ye et al, 2020b;Ghahari et al, 2022;Yu et al, 2022;Dan et al, 2021;Shu et al, 2019;Yoon et al, 2022;Lai et al, 2022) Performance prediction (Yu et al, 2022;Jiang et al, 2021aJiang et al, , 2021bZhao et al, 2022) Scenario simulation (Baisthakur and Chakraborty, 2021;Van Nimmen et al, 2021;Jiang et al, 2022a)…”

“…Updating the nDT of bridges enables the assessment of structural conditions, and various approaches have been explored. Ghahari et al (2022) proposed continuous Bayesian model updating to modify the bridge's finite element model (FEM) for evaluating damage during seismic events. Yu et al (2022) analyzed stress response, considering vehicle loads and temperature effects on the fatigue damage state, by updating the digital twin numerical model with data from the dynamic weighing and SHM systems.…”

A Two-step FE Model Updating Approach for System and Damage Identification of Full-Scale Prestressed Bridge Girders

“…The average age of in-service bridges in the United States is increasing, which necessitates methods and tools to inform decision making related to maintenance and/or replacement of these structures [1]. Finite Element (FE) model updating methods have emerged as a venerable procedure for operational health monitoring and post-event structural damage identification [2][3][4][5][6][7][8][9][10][11][12][13][14][15]. In these methods, the initial/baseline FE model -developed using available as-built Disclaimer/Publisher's Note: The statements, opinions, and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s).…”