Structural damage identification based on residual force vector

2007 ◽  
Vol 305 (1-2) ◽  
pp. 298-307 ◽  
Author(s):  
Q.W. Yang ◽  
J.K. Liu
Keyword(s):  
Structural Damage ◽  
Damage Identification ◽  
Force Vector ◽  
Structural Damage Identification ◽  
Residual Force

Baseline updating method for structural damage identification using modal residual force and grey wolf optimization

2019 ◽  
Vol 52 (4) ◽  
pp. 549-566 ◽  
Author(s):  
Ali Zare Hosseinzadeh ◽  
Gholamreza Ghodrati Amiri ◽  
Mojtaba Jafarian Abyaneh ◽  
Seyed Ali Seyed Razzaghi ◽  
Azadeh Ghadimi Hamzehkolaei
Keyword(s):  
Structural Damage ◽  
Damage Identification ◽  
Grey Wolf ◽  
Grey Wolf Optimization ◽  
Structural Damage Identification ◽  
Residual Force

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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