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 Identification of Composite Materials Based on PNN

2010 ◽  
Vol 29-32 ◽  
pp. 642-645
Author(s):  
Xiao Ma Dong
Keyword(s):  
Neural Network ◽  
Composite Materials ◽  
Bp Neural Network ◽  
Structural Damage ◽  
Damage Identification ◽  
Performance Comparison ◽  
Measurement Noise ◽  
Dynamic Approach ◽  
Numerical Examples ◽  
Structural Damage Identification

In recent years, there were been increasing researches focusing on the application of artificial neural networks in structural damage identification. Most of them perform well with numerical examples under error-free conditions, but become worse when the experimental data are polluted with measurement noise. In this paper, a dynamic approach based on PNN for damage identification of composite materials was proposed. By using wavelet series, the features of signals were extracted and input to PNN for training the network and identifying the damages. A performance comparison between the PNN and BPNN for structural damage identification was carried out. The results show that the proposed method can more exactly identify the faults than the BP neural network.

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.

scholarly journals Multi-Damage Identification of Multi-Span Bridges Based on Influence Lines

Coatings ◽  
2021 ◽  
Vol 11 (8) ◽  
pp. 905
Author(s):  
Yunkai Zhang ◽  
Qingli Xie ◽  
Guohua Li ◽  
Yali Liu
Keyword(s):  
Traffic Engineering ◽  
Structural Damage ◽  
Damage Identification ◽  
Work Conditions ◽  
Continuous Beam ◽  
Influence Line ◽  
Continuous Beam Bridge ◽  
Second Derivatives ◽  
Structural Damage Identification ◽  
Beam Bridge

The framework security of a bridge is essential as a critical component of traffic engineering. Even though the bridge structure is damaged to various degrees due to various reasons, the bridge will be wrecked when the damage reaches a particular level, suggesting a negative influence on people’s lives. Based on the current situation and existing problems of structural damage identification of bridges, a structural damage identification technology of continuous beam bridges based on deflection influence lines is proposed in this paper in order to keep track of and always detect broken bridge elements, thereby extending the bridge’s service life and reducing the risk of catastrophic accidents. The line function expression of deflection impact on a multi-span continuous beam bridge was first obtained using Graphic Multiplication theory. From the theoretical level, the influence line function of the continuous beam bridge without extensive damage was computed, and a graph was generated. The photographs of the DIL as well as the first and second derivatives, the deflection influence line distinction and its first and second derivatives, and the DIL distinction and its first and second derivatives of a continuous beam bridge in a single position and multi-position destruction were fitted in this paper. Finally, after comparing multiple work conditions and multiple measuring points, it was found that the first derivative of deflection influence line difference had the best damage identification effect. The design was completed and tested, which had verified the feasibility of this theory.

Optimal Sensor Placement Algorithm for Structural Damage Identification

2020 ◽  
Vol 14 (1) ◽  
pp. 69-81
Author(s):  
C.H. Li ◽  
Q.W. Yang
Keyword(s):  
Structural Damage ◽  
Damage Identification ◽  
Sensor Placement ◽  
Optimization Procedure ◽  
Frame Structure ◽  
Truss Structures ◽  
Optimal Sensor Placement ◽  
Structural Damage Identification ◽  
Placement Algorithm ◽  
Sensor Locations

Background: Structural damage identification is a very important subject in the field of civil, mechanical and aerospace engineering according to recent patents. Optimal sensor placement is one of the key problems to be solved in structural damage identification. Methods: This paper presents a simple and convenient algorithm for optimizing sensor locations for structural damage identification. Unlike other algorithms found in the published papers, the optimization procedure of sensor placement is divided into two stages. The first stage is to determine the key parts in the whole structure by their contribution to the global flexibility perturbation. The second stage is to place sensors on the nodes associated with those key parts for monitoring possible damage more efficiently. With the sensor locations determined by the proposed optimization process, structural damage can be readily identified by using the incomplete modes yielded from these optimized sensor measurements. In addition, an Improved Ridge Estimate (IRE) technique is proposed in this study to effectively resist the data errors due to modal truncation and measurement noise. Two truss structures and a frame structure are used as examples to demonstrate the feasibility and efficiency of the presented algorithm. Results: From the numerical results, structural damages can be successfully detected by the proposed method using the partial modes yielded by the optimal measurement with 5% noise level. Conclusion: It has been shown that the proposed method is simple to implement and effective for structural damage identification.

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