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.

Damage Identification of Offshore Platform Based on D-S Evidence Theory

2011 ◽  
Vol 255-260 ◽  
pp. 314-318
Author(s):  
Yan Song Diao ◽  
Xian Neng Tong
Keyword(s):  
Structural Damage ◽  
Damage Identification ◽  
Evidence Theory ◽  
Offshore Platform ◽  
Damage Factor ◽  
Modal Strain Energy ◽  
Modal Strain Energy Method ◽  
Damage Probability ◽  
Structural Damage Identification ◽  
Theory Formula

In order to improve the accuracy of damage identification of offshore platform, structural damage identification method based on D-S evidence theory is proposed. Firstly, the stiffness matrix damage factor iterative method and modal strain energy method are used to identify the structural damage, and give the decision vectors respectively. Then, the decision vectors are imported into the D-S evidence theory formula, the damage probability of every element would be obtained and the damage location would be determined finally. Offshore platform model experiments show that the results of structural damage identification are more accurate and reliable after D-S evidence theory is used.

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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