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An efficient hybrid TLBO-PSO-ANN for fast damage identification in steel beam structures using IGA

(2020) SMART STRUCTURES AND SYSTEMS. 25(5). p.605-617
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Abstract
The existence of damages in structures causes changes in the physical properties by reducing the modal parameters. In this paper, we develop a two-stages approach based on normalized Modal Strain Energy Damage Indicator (nMSEDI) for quick applications to predict the location of damage. A two-dimensional IsoGeometric Analysis (2D-IGA), Machine Learning Algorithm (MLA) and optimization techniques are combined to create a new tool. In the first stage, we introduce a modified damage identification technique based on frequencies using nMSEDI to locate the potential of damaged elements. In the second stage, after eliminating the healthy elements, the damage index values from nMSEDI are considered as input in the damage quantification algorithm. The hybrid of Teaching-Learning-Based Optimization (TLBO) with Artificial Neural Network (ANN) and Particle Swarm Optimization (PSO) are used along with nMSEDI. The objective of TLBO is to estimate the parameters of PSO-ANN to find a good training based on actual damage and estimated damage. The IGA model is updated using experimental results based on stiffness and mass matrix using the difference between calculated and measured frequencies as objective function. The feasibility and efficiency of nMSEDI-PSO-ANN after finding the best parameters by TLBO are demonstrated through the comparison with nMSEDI-IGA for different scenarios. The result of the analyses indicates that the proposed approach can be used to determine correctly the severity of damage in beam structures.
Keywords
IsoGeometric Analysis, damage identification, TLBO, PSO-ANN, dynamic analysis, TOPOLOGY OPTIMIZATION, ISOGEOMETRIC ANALYSIS, NEURAL-NETWORK, VIBRATION, ALGORITHM, INDICATOR, NURBS, FRF

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MLA
Khatir, Samir, et al. “An Efficient Hybrid TLBO-PSO-ANN for Fast Damage Identification in Steel Beam Structures Using IGA.” SMART STRUCTURES AND SYSTEMS, vol. 25, no. 5, 2020, pp. 605–17, doi:10.12989/sss.2020.25.5.605.
APA
Khatir, S., Khatir, T., Boutchicha, D., Le Thanh, C., Tran, H., Bui, T. Q., … Abdel Wahab, M. (2020). An efficient hybrid TLBO-PSO-ANN for fast damage identification in steel beam structures using IGA. SMART STRUCTURES AND SYSTEMS, 25(5), 605–617. https://doi.org/10.12989/sss.2020.25.5.605
Chicago author-date
Khatir, Samir, T. Khatir, D. Boutchicha, C. Le Thanh, Hoa Tran, T.Q. Bui, R. Capozucca, and Magd Abdel Wahab. 2020. “An Efficient Hybrid TLBO-PSO-ANN for Fast Damage Identification in Steel Beam Structures Using IGA.” SMART STRUCTURES AND SYSTEMS 25 (5): 605–17. https://doi.org/10.12989/sss.2020.25.5.605.
Chicago author-date (all authors)
Khatir, Samir, T. Khatir, D. Boutchicha, C. Le Thanh, Hoa Tran, T.Q. Bui, R. Capozucca, and Magd Abdel Wahab. 2020. “An Efficient Hybrid TLBO-PSO-ANN for Fast Damage Identification in Steel Beam Structures Using IGA.” SMART STRUCTURES AND SYSTEMS 25 (5): 605–617. doi:10.12989/sss.2020.25.5.605.
Vancouver
1.
Khatir S, Khatir T, Boutchicha D, Le Thanh C, Tran H, Bui TQ, et al. An efficient hybrid TLBO-PSO-ANN for fast damage identification in steel beam structures using IGA. SMART STRUCTURES AND SYSTEMS. 2020;25(5):605–17.
IEEE
[1]
S. Khatir et al., “An efficient hybrid TLBO-PSO-ANN for fast damage identification in steel beam structures using IGA,” SMART STRUCTURES AND SYSTEMS, vol. 25, no. 5, pp. 605–617, 2020.
@article{8665304,
  abstract     = {The existence of damages in structures causes changes in the physical properties by reducing the modal parameters. In this paper, we develop a two-stages approach based on normalized Modal Strain Energy Damage Indicator (nMSEDI) for quick applications to predict the location of damage. A two-dimensional IsoGeometric Analysis (2D-IGA), Machine Learning Algorithm (MLA) and optimization techniques are combined to create a new tool. In the first stage, we introduce a modified damage identification technique based on frequencies using nMSEDI to locate the potential of damaged elements. In the second stage, after eliminating the healthy elements, the damage index values from nMSEDI are considered as input in the damage quantification algorithm. The hybrid of Teaching-Learning-Based Optimization (TLBO) with Artificial Neural Network (ANN) and Particle Swarm Optimization (PSO) are used along with nMSEDI. The objective of TLBO is to estimate the parameters of PSO-ANN to find a good training based on actual damage and estimated damage. The IGA model is updated using experimental results based on stiffness and mass matrix using the difference between calculated and measured frequencies as objective function. The feasibility and efficiency of nMSEDI-PSO-ANN after finding the best parameters by TLBO are demonstrated through the comparison with nMSEDI-IGA for different scenarios. The result of the analyses indicates that the proposed approach can be used to determine correctly the severity of damage in beam structures.},
  author       = {Khatir, Samir and Khatir, T. and Boutchicha, D. and Le Thanh, C. and Tran, Hoa and Bui, T.Q. and Capozucca, R. and Abdel Wahab, Magd},
  issn         = {1738-1584},
  journal      = {SMART STRUCTURES AND SYSTEMS},
  keywords     = {IsoGeometric Analysis,damage identification,TLBO,PSO-ANN,dynamic analysis,TOPOLOGY OPTIMIZATION,ISOGEOMETRIC ANALYSIS,NEURAL-NETWORK,VIBRATION,ALGORITHM,INDICATOR,NURBS,FRF},
  language     = {eng},
  number       = {5},
  pages        = {605--617},
  title        = {An efficient hybrid TLBO-PSO-ANN for fast damage identification in steel beam structures using IGA},
  url          = {http://dx.doi.org/10.12989/sss.2020.25.5.605},
  volume       = {25},
  year         = {2020},
}

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