Finite element model updating using strain-based power spectral density for damage detection

Summary In structural health monitoring, one practical challenge is to separate the change of structural characteristics (e.g., natural frequency and mode shape) due to environmental impacts from those induced by actual damage. Generally, data‐driven regression models are applied to remove the environmental impacts before model updating takes place. Model selection and training procedures are required in constructing these regression models, which are often subjective, prone to overfitting issue, and human errors. This paper proposes a novel physics‐based Environmental‐Effects‐Embedded model updating method. By embedding physical mechanisms of environmental impacts into the formulation of the finite element model, the proposed method is capable of considering these impacts during the finite element model updating. A comparative numerical study is performed by applying both the Environmental‐Effects‐Embedded model updating method and traditional method on a pedestrian bridge model subjected to structural damage, temperature variation, and boundary condition change. Comparison between the proposed and traditional methods has demonstrated that the proposed method can offer more accurate results in localizing and quantifying the structural damage under environmental impacts.

Section: Numerical Studysupporting

confidence: 87%

Environmental-effects-embedded model updating method considering environmental impacts

Zhou

Song

2017

“…Because these are formulated as the combination of the modal data and the inherent structural properties, it is possible to define reliable and robust FEMU and/or damage detection approaches by using the energy-based modal functions. [10][11][12][13][14][15] Sensitivity analysis is taken into account as a powerful and tried-and-trusted method for FEMU. 1 In general, sensitivity-based FEMU techniques lie in taking the first-order derivative (gradient) of dynamic outputs with respect to the structural parameters.…”

Section: Discussionmentioning

confidence: 99%

“…As an alternative, it is feasible to apply energy‐based modal functions such as modal strain energy (MSE) and modal kinetic energy (MKE) to the mentioned applications. Because these are formulated as the combination of the modal data and the inherent structural properties, it is possible to define reliable and robust FEMU and/or damage detection approaches by using the energy‐based modal functions …”

Section: Introductionmentioning

confidence: 99%

A sensitivity‐based finite element model updating based on unconstrained optimization problem and regularized solution methods

Rezaiee‐Pajand

Entezami

Sarmadi

2020

Summary An effective and reliable approach to updating finite element (FE) models of real structures is to utilize a sensitivity‐based strategy. A challenging issue concerning the sensitivity‐based finite element model updating (FEMU) is to create a well‐established framework for updating the inherent structural properties of FE models under incomplete noisy modal data. When noise contaminates the measured modal parameters, another challenging issue stems from the ill‐posedness of the FEMU inverse problem. This article proposes an innovative sensitivity‐based FEMU strategy based on the combination of modal kinetic energy and modal strain energy for simultaneously updating the element mass and stiffness matrices of FE models. The great novelty of this strategy is to get an idea from the unconstrained optimization problem for the establishment of a sensitivity‐based FEMU framework. The correction of the element mass and stiffness matrices in a simultaneous way is another novelty of the proposed FEMU strategy. Moreover, new iterative and hybrid regularization methods under the Krylov subspace theory and bidiagonalization process are presented to solve the ill‐posed inverse problem of FEMU. The accuracy and reliability of the proposed methods are numerically validated by a two‐story concrete frame and a two‐span continuous steel truss along with some comparative analyses. Results demonstrate that the suggested sensitivity‐based strategy and regularized solution methods are influential and successful in FEMU under incomplete noisy modal data.

“…Later, Chatzielefttheriou and Lagaros 5 proposed a new two-loop trajectory method to conduct damage identification. Furthermore, the spectral density method 6 , the Bayesian method [7][8][9][10][11][12] , and the Kalman filter 13 have been, respectively, employed in the vibration-based structural identification.…”

Section: Introductionmentioning

confidence: 99%

Structural damage identification by sparse deep belief network using uncertain and limited data

Ding

Hao

2020

Summary The accuracy of structural damage identification is affected by the uncertainties in the vibration measurements and the finite element modeling. This paper proposes a novel approach based on sparse deep belief network (DBN) for structural damage identification with uncertain and limited data. Vibration characteristics, that is, natural frequencies and mode shapes, are extracted as the input to the network, while the output are the damage locations and severities of the structure. DBN is chosen to train the generated data sets and identify structural damages. Restricted Boltzmann Machines (RBMs) are used as building blocks to composite a DBN. To further enhance the capacity of the RBMs, an arctan‐based sparse constraint is utilized to enable the hidden units to become sparse. This is achieved by adding an arctan norm constraint on the whole of the hidden units' activation probabilities. Numerical and experimental studies are conducted to verify the accuracy and performance of the proposed method. Undetermined damage identification is conducted, in which the quantity of input modal data is less than that of the system parameters to be identified. The identification results show that the proposed sparse DBN based on arctan can identify the damage effectively, and its accuracy is better than those obtained by other methods, even when the modeling uncertainty and the measurement noise exist and only limited data is available.