Bayesian model updating with finite element vs surrogate models: Application to a miter gate structural system

Journal Article · Thu Sep 29 00:00:00 EDT 2022 · Engineering Structures

DOI:https://doi.org/10.1016/j.engstruct.2022.114901· OSTI ID:2446660

Ramancha, Mukesh K. ^[1]; Vega, Manuel Alejandro ^[2]; Conte, Joel P. ^[1]; Todd, Michael Douglas ^[1]; Hu, Zhen ^[3]

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1. Univ. of California, San Diego, CA (United States)
2. Los Alamos National Laboratory (LANL), Los Alamos, NM (United States)
3. University of Michigan-Dearborn, MI (United States)

Bayesian finite element (FE) model updating using direct model evaluations of large-scale high-fidelity FE models is extremely computationally expensive. Surrogate models can be used as fast emulators of FE models to accelerate the model calibration process. The physics/mechanics-based FE models are still the underpinning behind the surrogate models. Here, this paper evaluates the loss in accuracy and the gain in computational time while performing Bayesian model updating by using surrogate model evaluations compared to using direct FE model evaluations. This evaluation is crucial before entirely relying on surrogate models in model updating for structural health monitoring (SHM) and damage prognosis (DP) purposes. This paper also demonstrates Bayesian updating and surrogate model construction of large-scale high-fidelity FE models of infrastructure systems. In this regard, the miter gate structural system is considered as the testbed structure. Three predominant damage modes (loss of contact between gate and wall, loss of thickness due to corrosion, and loss of tension in the diagonal rods) are considered for model updating purposes. Bayesian model updating is performed using direct FE evaluations by leveraging parallel computing. Two types of surrogates, namely polynomial chaos expansion (PCE) and Gaussian process regression (GPR), are developed for the miter gate. Model updating is performed again using the trained surrogate models, and the updating results are compared with their counterparts obtained using the direct FE evaluation results. The posterior distribution of the FE model parameters obtained using the trained surrogates are sufficiently accurate with respect to the posterior obtained utilizing the direct FE evaluations. In addition, an approximate 4-fold decrease in the computational time was observed when using surrogate model evaluations instead of direct FE evaluations for model updating.

• Research Organization: Los Alamos National Laboratory (LANL), Los Alamos, NM (United States)
• Sponsoring Organization: US Army Corps of Engineers; USDOE National Nuclear Security Administration (NNSA)
• Grant/Contract Number: 89233218CNA000001
• OSTI ID: 2446660
• Report Number(s): LA-UR--21-28985
• Journal Information: Engineering Structures, Journal Name: Engineering Structures Vol. 272; ISSN 0141-0296
• Publisher: Elsevier
• Country of Publication: United States
• Language: English

Related Subjects

42 ENGINEERING
Bayesian model updating
Gaussian process regression
high-fidelity FE models
large-scale systems
miter gate
polynomial chaos expansion
surrogate modeling