Structural damage identification based on modal frequency strain energy assurance criterion and flexibility using enhanced Moth-Flame optimization

Structures ◽  
2020 ◽  
Vol 28 ◽  
pp. 1119-1136
Author(s):  
Minshui Huang ◽  
Xifan Li ◽  
Yongzhi Lei ◽  
Jianfeng Gu
Keyword(s):  
Structural Damage ◽  
Strain Energy ◽  
Damage Identification ◽  
Modal Frequency ◽  
Structural Damage Identification

Detection Indicator of Structural Nondestructive Damage Based on Flexibility Curvature Difference Rate

2015 ◽  
Vol 744-746 ◽  
pp. 46-52 ◽  
Author(s):  
Chang Sheng Xiang ◽  
Yu Zhou ◽  
Sheng Kui Di ◽  
Li Xian Wang ◽  
Jian Shu Cheng
Keyword(s):  
Structural Damage ◽  
Damage Identification ◽  
Continuous Beam ◽  
Change Rate ◽  
Modal Frequency ◽  
Numerical Examples ◽  
Modal Flexibility ◽  
Structural Damage Identification ◽  
Before And After ◽  
Better Than

Applied to the structural damage identification, Modal Flexibility is better than the Modal Frequency and Modal Displacement, the indicators of Flexibility Curvature are effective and sensitive. This paper proposes a new detection indicator which is Flexibility Curvature Difference Rate (FCDR) that by using the change rate of diagonal elements of flexibility curvature difference when before and after damage. The numerical examples of a simple beam, a continuous beam and a frame with the damage conditions of the different positions and different degrees are used to verify FCDR. The result shows that FCDR can well identify the numerical examples damages, and sensitively diagnose the damage near the supports of beam and the nodes of framework.

Damage Severity Assessment Using Modified BP Neural Network

2011 ◽  
Vol 179-180 ◽  
pp. 1016-1020
Author(s):  
Xiao Ma Dong ◽  
Zhong Hui Wang
Keyword(s):  
Neural Network ◽  
Composite Material ◽  
Bp Neural Network ◽  
Structural Damage ◽  
Simulation Experiment ◽  
Damage Identification ◽  
Severity Assessment ◽  
Improved Method ◽  
Modal Frequency ◽  
Structural Damage Identification

Damage severity identification is an important content among structural damage identification. In order to avoid the disadvantages of conventional BPNN, a modified BP neural network was proposed to identify structural damage severity in this paper. The modified BPNN was trained by using structural modal frequency qua BPNN input, and then used to forecast structural damage severity. Finally, the results of simulation experiment of composite material cantilever girder show that the improved method is very effective for damage severity identification and possess great applied foreground.

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