Structural damage detection by a new iterative regularization method and an improved sensitivity function

Entezami, Alireza; Shariatmadar, Hashem; Sarmadi, Hassan

doi:10.1016/j.jsv.2017.02.038

Cited by 40 publications

(24 citation statements)

References 40 publications

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“…In structural dynamics and modal analysis, the strain energy is stored in each element of the structure when the eigenvectors are equivalent to the nodal displacements. 20 This kind of energy is known as the MSE when the modal parameters are the main vibration characteristics. On the other hand, the MKE is associated with the kinetic energy in dynamics and related to the mass of structural systems.…”

Section: Modal Kinetic and Strain Energiesmentioning

confidence: 99%

“…Moreover, m j and k j denote the jth element mass and stiffness matrices of the FE model. Equations (21) and (22) are substituted into Equation (20) to obtain…”

Section: An Innovative Sensitivity-based Femu Strategymentioning

confidence: 99%

“…All The lengths of the column and beam elements correspond to 0.75 and 1 m, respectively. For the simulation of real conditions of dynamic tests, it is assumed that some sensors are installed at the nodes 3,5,7,9,12,14,16,18,20, and 23 to measure the only translation modal displacements as illustrated in Figure 1. Moreover, the first seven modes are utilized to simulate the incompleteness of modal data.…”

Section: A Two-story Concrete Framementioning

confidence: 99%

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

Rezaiee‐Pajand

Entezami

Sarmadi

2020

Struct Control Health Monit

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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.

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Section: Modal Kinetic and Strain Energiesmentioning

confidence: 99%

“…Moreover, m j and k j denote the jth element mass and stiffness matrices of the FE model. Equations (21) and (22) are substituted into Equation (20) to obtain…”

Section: An Innovative Sensitivity-based Femu Strategymentioning

confidence: 99%

Section: A Two-story Concrete Framementioning

confidence: 99%

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

Rezaiee‐Pajand

Entezami

Sarmadi

2020

Struct Control Health Monit

Self Cite

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“…Tikhonov regularization is one of the most popular regularization techniques that can damp out the small measurement errors and single out a relatively accurate solution [15]. So far, it has been widely applied to damage detection [16][17][18] and other related research, such as model updating [19,20] and load identification [21]. Björck [22] gave a general form of Tikhonov regularization as follows:…”

Section: Introductionmentioning

confidence: 99%

Robust Structural Damage Detection Using Analysis of the CMSE Residual’s Sensitivity to Damage

Wang

Guo

et al. 2020

Applied Sciences

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This paper presents a robust damage identification scheme in which damage is predicted by solving the cross-modal strain energy (CMSE) linear system of equations. This study aims to address the excessive equations issue faced in the assemblage of the CMSE system. A sensitivity index that, to some extent, measures how the actual damage level vector satisfies each CMSE equation, is derived by performing an analysis of the defined residual’s sensitivity to damage. The index can be used to eliminate redundant equations and enhance the robustness of the CMSE system. Moreover, to circumvent a potentially ill-conditioned problem, a previously published iterative Tikhonov regularization method is adopted to solve the CMSE system. Some improvements to this method for determining the iterative regularization parameter and regularization operator are given. The numerical robustness of the proposed damage identification scheme against measurement noise is proved by analyzing a 2-D truss structure. The effects of location and extent of damage on the damage identification results are investigated. Furthermore, the feasibility of the proposed scheme for damage identification is experimentally validated on a beam structure.

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“…Chen and Muang [39] investigated dynamic perturbation theory for incomplete noisy modal data and used the L-curve approach for regularization parameter determination. Entezami et al [40] proposed a new sensitivity-based damage detection method in which an improved sensitivity function related to modal strain energy and modified GCV is presented. Grip et al [41] proposed a new regularization method that is based on the minimization of total variation on sensitivity-based model updating.…”

Section: Introductionmentioning

confidence: 99%

Structural Damage Detection with Different Objective Functions in Noisy Conditions Using an Evolutionary Algorithm

et al. 2017

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Dynamic properties such as natural frequencies and mode shapes are directly affected by damage in structures. In this paper, changes in natural frequencies and mode shapes were used as the input to various objective functions for damage detection. Objective functions related to natural frequencies, mode shapes, modal flexibility and modal strain energy have been used, and their performances have been analyzed in varying noise conditions. Three beams were analyzed: two of which were simulated beams with single and multiple damage scenarios and one was an experimental beam. In order to do this, SAP 2000 (v14, Computers and Structures Inc., Berkeley, CA, United States, 2009) is linked with MATLAB (r2015, The MathWorks, Inc., Natick, MA, United States, 2015). The genetic algorithm (GA), an evolutionary algorithm (EA), was used to update the damaged structure for damage detection. Due to the degradation of the performance of objective functions in varying noisy conditions, a modified objective function based on the concept of regularization has been proposed, which can be effectively used in combination with EA. All three beams were used to validate the proposed procedure. It has been found that the modified objective function gives better results even in noisy and actual experimental conditions.

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Structural damage detection by a new iterative regularization method and an improved sensitivity function

Cited by 40 publications

References 40 publications

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

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

Robust Structural Damage Detection Using Analysis of the CMSE Residual’s Sensitivity to Damage

Structural Damage Detection with Different Objective Functions in Noisy Conditions Using an Evolutionary Algorithm

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