Structural Damage Identification Under Temperature Variations Based on PSO–CS Hybrid Algorithm

Huang, Minshui; Cheng, Shaoxi; Zhang, Haiyang; Gül, Mustafa; Lu, Hailin

doi:10.1142/s0219455419501396

Cited by 24 publications

(16 citation statements)

References 52 publications

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“…Livani et al [10] proposed an enhanced particle swarm optimization (PSO) with a strategy called active/inactive flaw (AIF) of damage identification in an Aluminum plate. In [11] The Cuckoo search (CS) algorithm merged with PSO was suggested to predict structural damages under temperature variation. Several metaheuristic algorithms exist, and although they all offer better performance in ill-posed problems, each engineering problem is unique in how output varies according to the change in the design variables.…”

Section: Introductionmentioning

confidence: 99%

Damage identification in steel plate using FRF and inverse analysis

Khatir

Wahab

Tiachacht

et al. 2021

Frattura Ed Integrità Strutturale

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Metaheuristic algorithms have known vast development in recent years. And their applicability in engineering projects is constantly growing; however, their deferent exploration and exploitation techniques cause the engineering problems to favor some algorithms over others. This paper studies damage identification in steel plates using Frequency Response Function (FRF) damage indicator to detect and localize the healthy and damaged structure. The study is formulated as an inverse analysis, investigating the performance of three new metaheuristic algorithms of Wild Horse Optimizer (WHO), Harris Hawks Optimization (HHO), and Arithmetic Optimization Algorithm (AOA). The objective function is based on measured and calculated FRF damage indicators. The results showed that the case of four damages with different damage severity levels presented a good challenge where the HWO algorithm was shown to have the best performance. Both in convergence speed and CPU time.

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Section: Introductionmentioning

confidence: 99%

Damage identification in steel plate using FRF and inverse analysis

Khatir

Wahab

Tiachacht

et al. 2021

Frattura Ed Integrità Strutturale

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“…Initially, a mathematical model for modification is established in accordance with the characteristics of comparison matrices, where the least square method is effectively employed [6,7], and the iterative algorithm is designed and performed to handle it subsequently [8]. Similarly, much research has been conducted successfully in such a framework based on the meta-heuristic algorithms, such as particle swarm optimization (PSO) [9], genetic algorithm (GA) [10], and ant colony optimization (ACO) [11], where the modification method proves reasonable and feasible and could also be extended to multiple engineering optimization problems [12][13][14].…”

Section: Introductionmentioning

confidence: 99%

Improving Bridge Expansion and Contraction Installation Replacement Decision System Using Hybrid Chaotic Whale Optimization Algorithm

Huang

2021

Applied Sciences

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Bridge expansion and contraction installation (BECI) has proved to be an essential component of the bridge structure due to its stability, comfort, and durability benefits. At present, traditional replacement technologies for modular type, comb plate type, and seamless type BECIs are widely applied worldwide. Nevertheless, it is unfortunate that the research conducted on decision-making (DM) approaches for the technical condition assessment and the optimal replacement plan selection of existing BECIs remain scarce, which results in the waste of resources and the increase in cost. Therefore, a BECI technical condition assessment approach, which contains specific on-site inspection regulations with both qualitative and quantitative descriptions, is proposed in this research, and a corresponding calculation program has been developed based on the MATLAB platform, which provides the basis for the necessity of replacement. Simultaneously, the hybrid chaotic whale optimization algorithm is designed and performed to improve and automate the process of optimal replacement plan selection under the assistance of the analytic hierarchy process (AHP), where both the achievement in consistency modification and the reservation of initial information are perused, and its superiority and effectiveness are verified via the comparative experimental analysis. The improved BECI replacement decision system is established, and the corresponding case study demonstrates that the proposed system in this research proves reasonable and feasible. The improved system can effectively assist bridge managers in making more informed operation and maintenance (O and M) decisions in actual engineering projects.

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“…At present, the updating of FE model is mainly to adjust the stiffness matrix and mass matrix in the FE model, or to update the physical parameters in these matrices, so that the updated model can be used to reasonably predict the structural real mechanical behaviors. By now, many deterministic model updating methods have been developed, which mainly include optimization based model updating methods [ 1 , 2 , 3 , 4 , 5 , 6 , 7 ], or FE based model updating methods [ 8 , 9 , 10 , 11 , 12 ], Bayesian model updating methods and recently developed model updating methods based on artificial intelligence [ 13 , 14 , 15 ].…”

Section: Introductionmentioning

confidence: 99%

A New Stochastic Model Updating Method Based on Improved Cross-Model Cross-Mode Technique

Chen

Huang

Tee

et al. 2021

Sensors

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This paper proposes a new stochastic model updating method to update structural models based on the improved cross-model cross-mode (ICMCM) technique. This new method combines the stochastic hybrid perturbation-Galerkin method with the ICMCM method to solve the model updating problems with limited measurement data and uncertain measurement errors. First, using the ICMCM technique, a new stochastic model updating equation with an updated coefficient vector is established by considering the uncertain measured modal data. Then, the stochastic model updating equation is solved by the stochastic hybrid perturbation-Galerkin method so as to obtain the random updated coefficient vector. Following that, the statistical characteristics of the updated coefficients can be determined. Numerical results of a continuous beam show that the proposed method can effectively cope with relatively large uncertainty in measured data, and the computational efficiency of this new method is several orders of magnitude higher than that of the Monte Carlo simulation method. When considering the rank deficiency, the proposed stochastic ICMCM method can achieve more accurate updating results compared with the cross-model cross-mode (CMCM) method. An experimental example shows that the new method can effectively update the structural stiffness and mass, and the statistics of the frequencies of the updated model are consistent with the measured results, which ensures that the updated coefficients are of practical significance.

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Structural Damage Identification Under Temperature Variations Based on PSO–CS Hybrid Algorithm

Cited by 24 publications

References 52 publications

Damage identification in steel plate using FRF and inverse analysis

Damage identification in steel plate using FRF and inverse analysis

Improving Bridge Expansion and Contraction Installation Replacement Decision System Using Hybrid Chaotic Whale Optimization Algorithm

A New Stochastic Model Updating Method Based on Improved Cross-Model Cross-Mode Technique

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