Structural Damage Identification Based on Support Vector Machine and Relative Change Value of Modal Flexibility

2008 ◽  
Author(s):  
Xiao-chun Fan ◽  
Yan-Yang ◽  
Tian-yi Liu
Keyword(s):  
Support Vector Machine ◽  
Structural Damage ◽  
Damage Identification ◽  
Support Vector ◽  
Modal Flexibility ◽  
Structural Damage Identification ◽  
Relative Change

A Review of the Research Progress of Structural Damage Identification Method Based on Computational Intelligence Techniques

2013 ◽  
Vol 444-445 ◽  
pp. 1494-1502 ◽  
Author(s):  
Li Feng Xiao ◽  
Hui Tian
Keyword(s):  
Computational Intelligence ◽  
Structural Damage ◽  
Rough Set Theory ◽  
Damage Identification ◽  
Fuzzy Theory ◽  
Evidence Theory ◽  
Research Progress ◽  
Support Vector ◽  
Structural Damage Identification ◽  
Complex Structural

This paper presents a comprehensive review of computational Intelligence (CI) technology applied in structural damage identification, clarifies the basic principles of computational intelligence techniques, as well as the applicable difficulties that exist in the field of structural damage identification (SDI) from 6 aspects: fuzzy theory, evidence theory, rough set theory, artificial neural networks, support vector machines and evolutionary computation, and then discussed the applicable prospects of computational Intelligence in SDI. It points out that the reasonable cross-fusion of a variety of CI method to specific research object is a necessary means for SDI research. For economy and practicality considerations, CI is suitable for highly integrated complex 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.

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.

Sign in / Sign up

Export Citation Format

Share Document