Enhanced optimization-based structural damage detection method using modal strain energy and modal frequencies

Ghasemi, Mohammad Reza; Nobahari, Mehdi; Shabakhty, Naser

doi:10.1007/s00366-017-0563-5

Cited by 34 publications

(12 citation statements)

References 21 publications

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“…The generalized flexibility matrix of the structure is used in their method to define the objective function of the optimization problem. Ghasemi et al [33] used the modal strain energy and a modified genetic algorithm to determine structural damage location and extent. Ashory et al [34] developed a new objective function based on weighted strain energy for damage assessment using genetic algorithm.…”

Section: Introductionmentioning

confidence: 99%

Damage Identification for Shear-Type Structures Using the Change of Generalized Shear Energy

2022

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Structural damage identification has become an important topic in the field of civil engineering in recent years. The shear-type structure, such as shear frame structure, is a common type used in civil engineering. In this paper, a damage identification method based on the change of generalized shear energy is proposed for shear-type structures. The main steps of the proposed method are as follows. Firstly, the element stiffness matrix in the structural finite element model is decomposed to obtain the elementary shear force vector. Secondly, the elementary generalized shear energy is calculated by the dot product of the vibration mode shape vector and the elementary shear force vector. Thirdly, structural damage locations can be determined by the changes of elementary generalized shear energy. Finally, more accurate damage localization and quantification are achieved by solving the mode shape sensitivity equation. A 20-storey numerical example and a three-storey experimental model are used to demonstrate the proposed damage identification algorithm. From the numerical and experimental results, it was found that the proposed approach can accurately identify the location and extent of the damage in the shear structures even if the data contain noise. It has been shown that the presented algorithm may be useful in the damage identification of shear-type structures.

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Section: Introductionmentioning

confidence: 99%

Damage Identification for Shear-Type Structures Using the Change of Generalized Shear Energy

2022

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“…have been successfully applied to many engineering fields such as material design, structural optimization, and damage identification. [24][25][26][27] In this article, an improved perforation design with oval curve which is mathematically defined by two controlling geometric parameters is implemented to replace the peanut-shaped hole in literature, [19] which involves three geometric parameters. It is worth pointing out that the reduction of controlling parameters provides great convenience in the structural design, computational analysis, and data set preparation of ML model.…”

Section: Introductionmentioning

confidence: 99%

“…[ 23 ] Recently, the effectiveness of the ML algorithms has been proved and they have been successfully applied to many engineering fields such as material design, structural optimization, and damage identification. [ 24–27 ]…”

Section: Introductionmentioning

confidence: 99%

Novel Planar Auxetic Metamaterial Perforated with Orthogonally Aligned Oval‐Shaped Holes and Machine Learning Solutions

2021

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Auxetic metamaterials with negative Poisson's ratio have attracted much attention due to their counterintuitive deformation behavior over the conventional engineering materials. However, it is difficult to describe the complex correlation between microstructure parameters and auxeticity by analytical or empirical solutions in the form of math expressions. Herein, the machine learning (ML) model with artificial neural network (ANN) is developed to analyze a novel planar auxetic metamaterial designed by introducing orthogonally aligned oval‐shaped perforations in solid base material, and its feasibility is demonstrated through the experimental and finite element method (FEM) solutions. It is found that the proposed structure involving less design parameters exhibits the best performance at the aspects of auxetic behavior and stress level than those with peanut‐shaped holes and elliptic holes. Moreover, the results of parameter analysis demonstrate that the present ML solution model can provide accurate predicting results rapidly for this problem, without the limitations of explicit solution expressions which are typically not available in practice. The ML model allows one to obtain the desired auxetic property by tailoring the geometric parameters effectively and accelerate auxetic metamaterial design.

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“…In the same year, Dinh-Cong et al [17] proposed an efficient technique for optimal sensor placement and damage detection in laminated composite structures, and Jaya algorithm was again adopted to solve the inverse problem. Ghasemi et al [18] proposed a two-stage damage detection method as an inverse method for truss structures via a modified genetic algorithm. Ghiasi et al [19] carried out a comprehensive study on damage detection based on machine learning and inverse optimization problem utilizing Colliding Bodies Optimization (CBO).…”

Section: Introductionmentioning

confidence: 99%

Boundary Strategy for Optimization-based Structural Damage Detection Problem using Metaheuristic Algorithms

Kaveh

Hosseini

Zaerreza

2020

Period. Polytech. Civil Eng.

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The present paper proposes a new strategy namely Boundary Strategy (BS) in the process of optimization-based damage detection using metaheuristic algorithms. This strategy gradually neutralizes the effects of structural elements that are healthy in the optimization process. BS causes the optimization method to find the optimum solution better than conventional methods that do not use the proposed BS. This technique improves both aspects of the accuracy and convergence speed of the algorithms in identifying and quantifying the damage. To evaluate the performance of the developed strategy, a new damage-sensitive cost function, which is defined based on vibration data of the structure, is optimized utilizing the Shuffled Shepherd Optimization Algorithm (SSOA). Different examples including truss, beam, and frame are investigated numerically in order to indicate the applicability of the proposed technique. The proposed approach is also applied to other well-known optimization algorithms including TLBO, GWO, and MFO. The obtained results illustrate that the proposed method improves the performance of the utilized algorithms in identifying and quantifying of the damaged elements, even for noise-contaminated data.

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Enhanced optimization-based structural damage detection method using modal strain energy and modal frequencies

Cited by 34 publications

References 21 publications

Damage Identification for Shear-Type Structures Using the Change of Generalized Shear Energy

Damage Identification for Shear-Type Structures Using the Change of Generalized Shear Energy

Novel Planar Auxetic Metamaterial Perforated with Orthogonally Aligned Oval‐Shaped Holes and Machine Learning Solutions

Boundary Strategy for Optimization-based Structural Damage Detection Problem using Metaheuristic Algorithms

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