Damage assessment in truss structures with limited sensors using a two-stage method and model reduction

Dinh-Cong, Du; Vo-Duy, T.; Nguyen-Thoi, T.

doi:10.1016/j.asoc.2018.02.028

Cited by 41 publications

(11 citation statements)

References 36 publications

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“…where [f i , φ i ] and [f k i , φ k i ] mean the i-th noise-free and contaminated natural frequencies and modal shapes, respectively; σ stands for the noise pollution level [38][39][40]; and rand is a random number that ranges from −1 to 1.…”

Section: Example Studiesmentioning

confidence: 99%

Bearing Damage Detection of a Bridge under the Uncertain Conditions Based on the Bayesian Framework and Matrix Perturbation Method

Lei

Zhou

et al. 2021

Shock and Vibration

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The bearing of a bridge, as a critical component, is important in the force transformation of the superstructure; however, due to the service condition and repeated impact load, the bearing is prone to be damaged but difficult to detect the damage; the present research has few studies that focused on the damage detection of the structural bearing. Meanwhile, practical engineering is always surrounded by variational environmental conditions, and sometimes, the element and bearing damage both exist in the structure. Thus, these uncertain conditions all cause inaccurate damage identification results using the vibration-based damage detection method. In order to detect the damage of the structural bearing and improve the precision, firstly, the structural dynamic characteristic equation considering uncertain conditions has been deduced; then, a damage detection framework constructed by the Bayesian theory and perturbation method has been developed in this article; a numerical example of an 8-span concrete continuous beam and a practical example of I-40 steel-concrete composite bridge are utilized to validate the feasibility of the proposed method, and single type and two types of damage cases are studied. The outcomes demonstrate that the damage of structural elements and bearings can be detected with high accuracy. The proposed method is of great applicability and good potential.

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Section: Example Studiesmentioning

confidence: 99%

Bearing Damage Detection of a Bridge under the Uncertain Conditions Based on the Bayesian Framework and Matrix Perturbation Method

Lei

Zhou

et al. 2021

Shock and Vibration

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“…Yin et al (2017) developed a probabilistic methodology based on FEM reduction technique and Bayesian inference for a bolted connection damage identification. Dinh-Cong et al (2018b) presented a two-stage method of damage assessment in truss structures. At first, they introduced a new damage indicator called normalized modal strain energy-based damage index, and they subsequently located the damage.…”

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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“…[27] Dinh-Cong et al introduced a damage identification method using teaching-learning-based optimization algorithm and Neumann series expansionbased model reduction method. [28] Some of the studies revolving around damage detection with limited data are included in precious studies. [29][30][31] Mirjalili et al have introduced some nature-inspired algorithms, [32][33][34][35][36] namely, grey wolf optimizer (GWO), moth-flame optimization (MFO), salp swarm algorithm (SSA), ant lion optimization (ALO), and whale optimization algorithm (WOA).…”

Section: Introductionmentioning

confidence: 99%

“…[ 27 ] Dinh‐Cong et al introduced a damage identification method using teaching–learning‐based optimization algorithm and Neumann series expansion‐based model reduction method. [ 28 ] Some of the studies revolving around damage detection with limited data are included in precious studies. [ 29–31 ]…”

Section: Introductionmentioning

confidence: 99%

Multiverse optimizer for structural damage detection: Numerical study and experimental validation

Ghannadi

Kourehli

2020

Structural Design Tall Build

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New optimization algorithms have been increasingly developed in the course of the recent years. The accuracy of identified results in optimization-based damage detection methods depends on the objective function and optimization algorithm. This paper employs multiverse Optimizer (MVO) to solve the optimization-based damage identification problem. Statistical results obtained by MVO are compared with those of sine cosine algorithm (SCA) and Harris hawks optimization (HHO) in order to perform a comparative study. Two objective functions are used in this optimization problem. The first one is based on the modal assurance criterion (MAC) and the second one on modified total modal assurance criterion (MTMAC). Numerical and experimental examples indicate that the combination of objective function based on MTMAC and MVO algorithm can provide accurate and reliable results in structural damage detection process. MAC is also rejected in competition with MTMAC. While using SCA-MTMAC, the accuracy of the results is acceptable in some cases. However, HHO-MTMAC gives weak results in most cases. MVO and SSA are the lowest, and HHO spends significantly more time in terms of computational cost.

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Damage assessment in truss structures with limited sensors using a two-stage method and model reduction

Cited by 41 publications

References 36 publications

Bearing Damage Detection of a Bridge under the Uncertain Conditions Based on the Bayesian Framework and Matrix Perturbation Method

Bearing Damage Detection of a Bridge under the Uncertain Conditions Based on the Bayesian Framework and Matrix Perturbation Method

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

Multiverse optimizer for structural damage detection: Numerical study and experimental validation

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