Fast Bayesian Approach for Stochastic Model Updating using Modal Information from Multiple Setups

Structural health monitoring plays a significant role in providing information regarding the performance of structures throughout their life spans. However, information that is directly extracted from monitored data is usually susceptible to uncertainties and not reliable enough to be used for structural investigations. Finite element model updating is an accredited framework that reliably identifies structural behavior. Recently, the modular Bayesian approach has emerged as a probabilistic technique in calibrating the finite element model of structures and comprehensively addressing uncertainties. However, few studies have investigated its performance on real structures. In this article, modular Bayesian approach is applied to calibrate the finite element model of a lab-scaled concrete box girder bridge. This study is the first to use the modular Bayesian approach to update the initial finite element model of a real structure for two states—undamaged and damaged conditions—in which the damaged state represents changes in structural parameters as a result of aging or overloading. The application of the modular Bayesian approach in the two states provides an opportunity to examine the performance of the approach with observed evidence. A discrepancy function is used to identify the deviation between the outputs of the experimental and numerical models. To alleviate computational burden, the numerical model and the model discrepancy function are replaced by Gaussian processes. Results indicate a significant reduction in the stiffness of concrete in the damaged state, which is identical to cracks observed on the body of the structure. The discrepancy function reaches satisfying ranges in both states, which implies that the properties of the structure are predicted accurately. Consequently, the proposed methodology contributes to a more reliable judgment about structural safety.

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A stochastic model updating method for parameter variability quantification based on response surface models and Monte Carlo simulation

Mechanical Systems and Signal Processing ◽

10.1016/j.ymssp.2012.06.028 ◽

2012 ◽

Vol 33 ◽

pp. 83-96 ◽

Cited By ~ 41

Author(s):

Sheng-En Fang ◽

Wei-Xin Ren ◽

Ricardo Perera

Keyword(s):

Monte Carlo Simulation ◽

Monte Carlo ◽

Stochastic Model ◽

Response Surface ◽

Model Updating ◽

Response Surface Models ◽

Surface Models

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Updating Predictive Models: Calibration, Bias Correction and Identifiability

Volume 1: 36th Design Automation Conference, Parts A and B ◽

10.1115/detc2010-28828 ◽

2010 ◽

Cited By ~ 3

Author(s):

Paul D. Arendt ◽

Wei Chen ◽

Daniel W. Apley

Keyword(s):

Computer Model ◽

Bayesian Approach ◽

Bias Correction ◽

Process Model ◽

Model Updating ◽

Future Research ◽

Model Parameters ◽

Sources Of Uncertainty ◽

Uncertain Model ◽

Many Sources

Model updating, which utilizes mathematical means to combine model simulations with physical observations for improving model predictions, has been viewed as an integral part of a model validation process. While calibration is often used to “tune” uncertain model parameters, bias-correction has been used to capture model inadequacy due to a lack of knowledge of the physics of a problem. While both sources of uncertainty co-exist, these two techniques are often implemented separately in model updating. This paper examines existing approaches to model updating and presents a modular Bayesian approach as a comprehensive framework that accounts for many sources of uncertainty in a typical model updating process and provides stochastic predictions for the purpose of design. In addition to the uncertainty in the computer model parameters and the computer model itself, this framework accounts for the experimental uncertainty and the uncertainty due to the lack of data in both computer simulations and physical experiments using the Gaussian process model. Several challenges are apparent in the implementation of the modular Bayesian approach. We argue that distinguishing between uncertain model parameters (calibration) and systematic inadequacies (bias correction) is often quite challenging due to an identifiability issue. We present several explanations and examples of this issue and bring up the needs of future research in distinguishing between the two sources of uncertainty.

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A stochastic model updating technique for complex aerospace structures

Finite Elements in Analysis and Design ◽

10.1016/j.finel.2011.02.005 ◽

2011 ◽

Vol 47 (7) ◽

pp. 739-752 ◽

Cited By ~ 43

Author(s):

B. Goller ◽

M. Broggi ◽

A. Calvi ◽

G.I. Schuëller

Keyword(s):

Stochastic Model ◽

Model Updating ◽

Aerospace Structures

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