An iterative total least squares‐based estimation method for structural damage identification of 3D frame structures

2020 ◽  
Vol 27 (4) ◽  
Author(s):  
Yingchao Li ◽  
Shuqing Wang ◽  
John Tapia ◽  
Zhipeng Xia ◽  
Wenzheng An
Keyword(s):  
Least Squares ◽  
Structural Damage ◽  
Damage Identification ◽  
Estimation Method ◽  
Total Least Squares ◽  
Frame Structures ◽  
Structural Damage Identification

scholarly journals Structural Damage Identification Based on AR Model with Additive Noises Using an Improved TLS Solution

Sensors ◽  
2019 ◽  
Vol 19 (19) ◽  
pp. 4341
Author(s):  
Wu ◽  
Li ◽  
Zhang
Keyword(s):  
Least Squares ◽  
Structural Damage ◽  
Shaking Table Test ◽  
Damage Identification ◽  
Shaking Table ◽  
Ar Model ◽  
Design Matrix ◽  
Damage Degree ◽  
Structural Damage Identification ◽  
Auto Regressive

Structural damage is inevitable due to the structural aging and disastrous external excitation. The auto-regressive (AR) based method is one of the most widely used methods for structural damage identification. In this regard, the classical least-squares algorithm is often utilized to solve the AR model. However, this algorithm generally could not take all the observed noises into account. In this study, a partial errors-in-variables (EIV) model is used so that both the current and prior observation errors are considered. Accordingly, a total least-squares (TLSE) solution is introduced to solve the partial EIV model. The solution estimates and accounts for the correlations between the current observed data and the design matrix. An effective damage indicator is chosen to count for damage levels of the structures. Both mathematical and finite element simulation results show that the proposed TLSE method yields better accuracy than the classical LS method and the AR model. Finally, the response data of a high-rise building shaking table test is used for demonstrating the effectiveness of the proposed method in identifying the location and damage degree of a model structure.

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