Structural damage detection based on incomplete modal data using pattern search algorithm

Kourehli, Seyed Sina; Amiri, G. Ghodrati; Ghafory‐Ashtiany, Mohsen; Bagheri, Abdollah

doi:10.1177/1077546312438428

Cited by 31 publications

(16 citation statements)

References 33 publications

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“…6 To do this, some random noise has been added to the theoretically calculated natural frequencies. Each contaminated frequency with noise can be obtained from the frequency without noise using the following equation:…”

Section: Simply Supported Beammentioning

confidence: 99%

“…Li et al 5 developed the cross-model cross-mode (CMCM) method for damage detection that is capable of identifying the damage to individual member of o®shore jacket platforms, when limited, spatially incomplete modal data were available. Also, Kourehli et al [6][7][8] presented the damage detection and estimation methods for structures based on incomplete modal data and incomplete static responses of a damaged structure via pattern search algorithm and simulated annealing algorithm. In other work, Kourehli 9 applied the arti¯cial neural network and incomplete noisy modal data to structural damage detection and estimation.…”

Section: Introductionmentioning

confidence: 99%

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LS-SVM Regression for Structural Damage Diagnosis Using the Iterated Improved Reduction System

Kourehli

2016

Int. J. Str. Stab. Dyn.

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A damage detection and estimation method is proposed for structural health monitoring using incomplete modal data and least squares support vector machine (LS-SVM). To accommodate the use of incomplete modal data, the iterated improved reduction system (IIRS) method has been used to condense the mass and sti®ness matrices of the structure. The¯rst two incomplete mode shapes and natural frequencies of a damaged structure are used as input data to the LS-SVM. The coupled simulated annealing (CSA) and standard simplex method using 10-fold cross-validation techniques are adopted to determine the optimal tuning parameters in the LS-SVM model. Three illustrative examples with and without noise in modal data are prepared to evaluate the performance of the proposed method. The results indicated that this method can be reliably used to identify the damages of structures with good accuracy. Int. J. Str. Stab. Dyn. Downloaded from www.worldscientific.com by FUDAN UNIVERSITY on 05/15/15. For personal use only.

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Section: Simply Supported Beammentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

LS-SVM Regression for Structural Damage Diagnosis Using the Iterated Improved Reduction System

Kourehli

2016

Int. J. Str. Stab. Dyn.

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“…Another set of structural damage detection methods employs an optimization basis for solving the damage identification problem (Chou and Ghaboussi 2001;Perera and Torres 2006;Perera, Fang, and Huerta 2009;Meruane and Heylen 2011;Bagheri, Razeghi, and Ghodrati Amiri 2012;Kourehli et al 2012;Perera, Marina, and Ruiz 2013;Saada, Arafa, and Nassef 2013). Teughels and De Roeck (2005) defined the damage identification procedure by a finite element model updating scheme and solved this problem by combining two traditional optimization algorithms.…”

Section: Introductionmentioning

confidence: 99%

Optimization-based method for structural damage localization and quantification by means of static displacements computed by flexibility matrix

Hosseinzadeh

Amiri

Koo

2015

Engineering Optimization

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This article presents an effective method for structural damage identification. The damage diagnosis problem is introduced as an optimization problem which is based on computing static displacements by the flexibility matrix. By utilizing this matrix, the complexity of the static displacement measurements in real cases can be overcome. The optimization problem is solved by a fast evolutionary optimization strategy, named the cuckoo optimization algorithm. The performance of the presented method was demonstrated by studying the benchmark problem provided by the IASC-ASCE Task Group on Structural Health Monitoring, and a numerical example of a frame. Moreover, the robustness of the presented approach was investigated in the presence of some prevalent modelling errors, and also when noisy and incomplete modal data are available. Finally, the efficiency of the proposed method was verified by an experimental study of a five-storey shear building structure. All the obtained results show the good performance of the presented method.

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“…For this purpose, there are a few strategies which have been dedicated to the research area. Several researchers used mode shape expansion techniques [13]- [15] and others employed model reduction techniques [16]- [18]. The objective of this work is to present a practical and workable method that attempts to deal with this challenge by applying a model reduction technique for reducing the initial FE model of a full structure to a reduced model relating to measured DOFs.…”

Section: Introductionmentioning

confidence: 99%

Damage Detection of 2D Frame Structures using Incomplete Measurements by Optimization Procedure and Model Reduction

Dinh-Cong

Pham-Duy²,

Nguyen-Thoi³

2018

J. ADV. ENG. COMPUT.

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The article presents an effective method for damage assessment of 2D frame structures using incomplete modal data by optimization procedure and model reduction technique. In this proposed method, the structural damage detection problem is defined as an optimization problem, in which a hybrid objective function and the damage severity of all elements are considered as the objective function and the continuous design variables, respectively. The teaching-learning-based optimization (TLBO) algorithm is applied as a powerful optimization tool to solve the problem. In addition, owing to the use of incomplete measurements, an improved reduction system (IRS) technique is adopted to reduce the mass and stiffness matrices of structural finite element model. The efficiency and robustness of the proposed method are validated with a 4-storey (3 bay) steel plane frame involving several damage scenarios without and with measurement noise. The obtained results clearly demonstrate that even the incompleteness and noisy environment of measured modal data, the present method can work properly in locating and estimating damage of the frame structure by utilizing only the first five incomplete modes' data. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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Structural damage detection based on incomplete modal data using pattern search algorithm

Cited by 31 publications

References 33 publications

LS-SVM Regression for Structural Damage Diagnosis Using the Iterated Improved Reduction System

LS-SVM Regression for Structural Damage Diagnosis Using the Iterated Improved Reduction System

Optimization-based method for structural damage localization and quantification by means of static displacements computed by flexibility matrix

Damage Detection of 2D Frame Structures using Incomplete Measurements by Optimization Procedure and Model Reduction

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