Structural damage detection using sparse sensors installation by optimization procedure based on the modal flexibility matrix

Hosseinzadeh, Ali Zare; Amiri, G. Ghodrati; Razzaghi, Seyed Ali Seyed; Koo, Ki-Young; Sung, Soo Hak

doi:10.1016/j.jsv.2016.06.037

Cited by 32 publications

(14 citation statements)

References 40 publications

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“…The method consists of three stages: (a) retrieving modal properties through a stochastic subspace identification algorithm, (b) reconstructing a reduced-order stiffness matrix through SEREP technique, and (c) reconstructing an FEM optimized with a PSO. Some of the other damage detection methods based on model reduction techniques can be found in Hosseinzadeh et al (2016Hosseinzadeh et al ( , 2017Hosseinzadeh et al ( , 2019 and Kourehli et al (2013).…”

Section: Introductionmentioning

confidence: 99%

Efficiency of grey wolf optimization algorithm for damage detection of skeletal structures via expanded mode shapes

Ghannadi

Kourehli

Noori

et al. 2020

Advances in Structural Engineering

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Vibration-based structural damage identification through optimization techniques has become an interesting research topic in recent years. Dynamic characteristics such as frequencies and mode shapes are used to construct the objective function. The objective functions based on only frequencies are not very sensitive to damage in large structures. However, objective functions based on both mode shapes and frequencies are very effective. In real measurement condition, the number of installed sensors is limited, and there are no economic reasons for measuring the mode shapes at all degrees of freedom. In this kind of circumstances, mode expansion methods are used to address the incompleteness of mode shapes. In this article, the system equivalent reduction and expansion process is applied to determine the unmeasured mode shapes. Two experimental examples including a cantilever beam and a truss tower are investigated to show system equivalent reduction and expansion process’ efficiency in estimating unmeasured mode shapes. The results show that the technique used for expansion is influential. Damage identification is formulated as an optimization problem, and the residual force vector based on expanded mode shapes is considered as an objective function. In order to minimize the objective function, grey wolf optimization and Harris hawks optimization are used. Numerical studies on a 56-bar dome space truss and experimental validation on a steel frame are performed to demonstrate the efficiency of the developed approach. Both numerical and experimental results indicate that the combination of the grey wolf optimization and expanded mode shapes with system equivalent reduction and expansion process can provide a reliable approach for determining the severities and locations of damage of skeletal structures when it compares with those obtained by Harris hawks optimization.

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Section: Introductionmentioning

confidence: 99%

Efficiency of grey wolf optimization algorithm for damage detection of skeletal structures via expanded mode shapes

Ghannadi

Kourehli

Noori

et al. 2020

Advances in Structural Engineering

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“…4 Hosseinzadeh et al detected and evaluated multi-site structural damage through optimizing an objective function based on modal assurance criterion (MAC) and modal flexibility matrix using the democratic particle swarm optimization (DPSO) algorithm. 5 In comparison, the data-based method is based on a statistical rather than a physical model of the structure. It generalizes damage identification to a supervised/unsupervised learning problem in the statistical pattern recognition paradigm.…”

Section: Introductionmentioning

confidence: 99%

A numerical study on multi‐site damage identification: A data‐driven method via constrained independent component analysis

Zhang

Sun

2020

Struct Control Health Monit

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Summary This paper presents a solution to the multi‐site structural damage identification problem using a data‐driven method and constrained independent component analysis (cICA). While existing studies in this field presented encouraging results for single‐site damage identification, limited research effort has been devoted to identifying multi‐site damage due its complexity. Efficient features for single‐site damage identification may lose their effectiveness when multi‐site damage occurs. This paper extracts damage‐sensitive features from the ICA outcome of the structural responses under certain excitations. The information on structural damage contained in the response is compacted into the mixing matrix by enforcing identical independent components to that of intact structures. Hence, the cICA can significantly reduce the feature dimension and preserve all the valuable information of damage. A case study indicates that the mixing matrix elements, when used as damage features, can distinguish multi‐site damage cases from single‐site damage cases and locate the single‐site damage. Furthermore, the mixing matrix columns of multi‐site damage cases exhibit distinct correlation with that of the corresponding single‐site damage cases. As a result, the proposed method can progressively locate the structural damage. Moreover, the present study has the potential to identify multi‐site damage identification in data‐driven structural health monitoring without requiring multi‐site damage data as a reference. This relieves the burden from data incompleteness when using data‐based damage identification methods and pattern recognition.

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“…Hosseinzadeh et al. 18 introduced an efficient objective function based on the modal assurance criterion (MAC) and modal flexibility matrix to detect and estimate structural damage. Bernagozzia et al.…”

Section: Introductionmentioning

confidence: 99%

A damage identification method for a thin plate structure based on PVDF sensors and strain mode

Cui

Peng

et al. 2019

Proceedings of the Institution of Mechanical Engineers, Part C:

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Damage identification methods for engineering structures based on vibration parameters have the advantages of easy detection and high precision; however, structural strain information is more sensitive to structural damage than displacement information. Traditional resistance strain sensors have low accuracy and poor stability when measuring structural strains. Therefore, this paper uses a highly sensitive polyvinylidene fluoride dynamic strain sensor to identify structural damage in a thin plate. The polyvinylidene fluoride sensor is used to obtain structural strain response information, and structural modal parameters are identified using operational modal identification methods based on the natural excitation technique and the eigensystem realization algorithm. This paper uses a damage index based on mode shape and flexibility. A new damage index based on the LU decomposition of the flexibility matrix is used to identify the damage of the thin plate structure. The effectiveness of the modal identification methods and the new damage index is validated via an elastic thin plate experiment. The results show that the modal identification method and the new damage index proposed in this paper can identify damage in a thin plate structure. Sensor comparison experiments also show that compared with a resistance strain sensor, the polyvinylidene fluoride sensor has higher damage sensitivity, better damage recognition and the ability to recognize farther from the sensor.

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Structural damage detection using sparse sensors installation by optimization procedure based on the modal flexibility matrix

Cited by 32 publications

References 40 publications

Efficiency of grey wolf optimization algorithm for damage detection of skeletal structures via expanded mode shapes

Efficiency of grey wolf optimization algorithm for damage detection of skeletal structures via expanded mode shapes

A numerical study on multi‐site damage identification: A data‐driven method via constrained independent component analysis

A damage identification method for a thin plate structure based on PVDF sensors and strain mode

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