Transfer-learning guided Bayesian model updating for damage identification considering modeling uncertainty

2022 ◽  
Vol 166 ◽  
pp. 108426
Author(s):  
Zhiming Zhang ◽  
Chao Sun ◽  
Beibei Guo
Keyword(s):  
Transfer Learning ◽  
Bayesian Model ◽  
Damage Identification ◽  
Model Updating ◽  
Modeling Uncertainty

Structural Damage Assessment Using Output-Only Measurement: Localization and Quantification

2013 ◽  
Author(s):  
Chin-Hsiung Loh ◽  
Min-Hsuan Tseng ◽  
Shu-Hsien Chao
Keyword(s):  
Damage Detection ◽  
Damage Assessment ◽  
Structural Damage ◽  
Damage Identification ◽  
Model Updating ◽  
Null Space ◽  
Singular Spectrum Analysis ◽  
Damage Index ◽  
Computation Efficiency ◽  
Output Only

One of the important issues to conduct the damage detection of a structure using vibration-based damage detection (VBDD) is not only to detect the damage but also to locate and quantify the damage. In this paper a systematic way of damage assessment, including identification of damage location and damage quantification, is proposed by using output-only measurement. Four level of damage identification algorithms are proposed. First, to identify the damage occurrence, null-space and subspace damage index are used. The eigenvalue difference ratio is also discussed for detecting the damage. Second, to locate the damage, the change of mode shape slope ratio and the prediction error from response using singular spectrum analysis are used. Finally, to quantify the damage the RSSI-COV algorithm is used to identify the change of dynamic characteristics together with the model updating technique, the loss of stiffness can be identified. Experimental data collected from the bridge foundation scouring in hydraulic lab was used to demonstrate the applicability of the proposed methods. The computation efficiency of each method is also discussed so as to accommodate the online damage detection.

scholarly journals Detection of Sparse Damages in Structures

2019 ◽  
Author(s):  
Natalia Sabourova ◽  
Niklas Grip ◽  
Ulf Ohlsson ◽  
Lennart Elfgren ◽  
Yongming Tu ◽  
...  
Keyword(s):  
Structural Damage ◽  
Damage Identification ◽  
Model Updating ◽  
Element Model ◽  
Total Variation Regularization ◽  
Regularization Technique ◽  
Spatial Properties ◽  
Ill Posed ◽  
Pros And Cons ◽  
Kirchhoff Plate Model

<p>Structural damage is often a spatially sparse phenomenon, i.e. it occurs only in a small part of the structure. This property of damage has not been utilized in the field of structural damage identification until quite recently, when the sparsity-based regularization developed in compressed sensing problems found its application in this field.</p><p>In this paper we consider classical sensitivity-based finite element model updating combined with a regularization technique appropriate for the expected type of sparse damage. Traditionally, (I), &#119897;2- norm regularization was used to solve the ill-posed inverse problems, such as damage identification. However, using already well established, (II), &#119897;l-norm regularization or our proposed, (III), &#119897;l-norm total variation regularization and, (IV), general dictionary-based regularization allows us to find damages with special spatial properties quite precisely using much fewer measurement locations than the number of possibly damaged elements of the structure. The validity of the proposed methods is demonstrated using simulations on a Kirchhoff plate model. The pros and cons of these methods are discussed.</p>

scholarly journals Probabilistic Damage Detection and Identification of Coupled Structural Parameters using Bayesian Model Updating with Added Mass

2021 ◽  
Author(s):  
jice zeng ◽  
Young Hoon Kim
Keyword(s):  
Convergence Rate ◽  
Damage Detection ◽  
Bayesian Model ◽  
Model Updating ◽  
Coupling Effect ◽  
Structural Parameters ◽  
Added Mass ◽  
Distribution Functions ◽  
Single Chain ◽  
Characteristic Equations

Damage detection inevitably involves uncertainties originated from measurement noise and modeling error. It may cause incorrect damage detection results if not appropriately treating uncertainties. To this end, vibration-based Bayesian model updating (VBMU) is developed to utilize vibration responses or modal parameters to identify structural parameters (e.g., mass and stiffness) as probability distribution functions (PDF) and uncertainties. However, traditional VBMU often assumes that mass is well known and invariant because simultaneous identification of mass and stiffness may yield an unidentifiable problem due to the coupling effect of the mass and stiffness. In addition, the posterior PDF in VBMU is usually approximated by single-chain based Markov Chain Monte Carlo (MCMC), leading to a low convergence rate and limited capability for complex structures. This paper proposed a novel VBMU to address the coupling effect and identify mass and stiffness by adding known mass. Two vibration data sets are acquired from original and modified systems with added mass, giving the new characteristic equations. Then, the posterior PDF is reformulated by measured data and predicted counterparts from new characteristic equations. For efficiently approximating the posterior PDF, Differential Evolutionary Adaptive Metropolis (DREAM) Algorithm are adopted to draw samples by running multiple Markov chains parallelly to enhance convergence rate and sufficiently explore possible solutions. Finally, a numerical example with a ten-story shear building and a laboratory-scale three-story frame structure are utilized to demonstrate the efficacy of the proposed VBMU framework. The results show that the proposed method can successfully identify both mass and stiffness, and their uncertainties. Reliable probabilistic damage detection can also be achieved.

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