Progresses and Perspectives of Damage Identification in Bridge Structure

2013 ◽  
Vol 405-408 ◽  
pp. 1632-1636
Author(s):  
Xiang Nan Wu ◽  
Xiao Guang Wang
Keyword(s):  
Health Monitoring ◽  
Structural Damage ◽  
Damage Identification ◽  
Bridge Engineering ◽  
Load Testing ◽  
Identification Methods ◽  
Identification Method ◽  
Manual Inspection ◽  
Structural Damage Identification ◽  
Statistical Identification

Structural damage identification is the core of Structural health monitoring system. The paper points out existing damage identification methods and analysis the application status and disadvantages of various methods. The research hotspots are summarized on considering the statistical identification method under uncertainty and the environmental impact of damage identification methods. Aiming at the characters of bridge engineering, the paper points out that a four-in-one multi-system damage identification method is the develop direction for bridge structural damage identification, which is established based on construction, load testing, health monitoring and manual inspection.

scholarly journals A Mahalanobis Distance Cumulant-Based Structural Damage Identification Method with IMFs and Fitting Residual of SHM Measurements

2020 ◽  
Vol 2020 ◽  
pp. 1-17
Author(s):  
Chuang Chen ◽  
Yinhui Wang ◽  
Tao Wang ◽  
Xiaoyan Yang
Keyword(s):  
Mahalanobis Distance ◽  
Structural Damage ◽  
Damage Identification ◽  
Feature Vector ◽  
Recursive Algorithm ◽  
Intrinsic Mode Functions ◽  
Data Set ◽  
Identification Method ◽  
Structural Damage Identification ◽  
Emd Method

Data-driven damage identification based on measurements of the structural health monitoring (SHM) system is a hot issue. In this study, based on the intrinsic mode functions (IMFs) decomposed by the empirical mode decomposition (EMD) method and the trend term fitting residual of measured data, a structural damage identification method based on Mahalanobis distance cumulant (MDC) was proposed. The damage feature vector is composed of the squared MDC values and is calculated by the segmentation data set. It makes the changes of monitoring points caused by damage accumulate as “amplification effect,” so as to obtain more damage information. The calculation method of the damage feature vector and the damage identification procedure were given. A mass-spring system with four mass points and four springs was used to simulate the damage cases. The results showed that the damage feature vector MDC can effectively identify the occurrence and location of the damage. The dynamic measurements of a prestress concrete continuous box-girder bridge were used for decomposing into IMFs and the trend term by the EMD method and the recursive algorithm autoregressive-moving average with the exogenous inputs (RARMX) method, which were used for fitting the trend term and to obtain the fitting residual. By using the first n-order IMFs and the fitting residual as the clusters for damage identification, the effectiveness of the method is also shown.

scholarly journals Optimization-Based Evolutionary Data Mining Techniques for Structural Health Monitoring

2020 ◽  
Vol 9 (1) ◽  
pp. 14-23 ◽  
Author(s):  
Meisam Gordan ◽  
Zubaidah Binti Ismail ◽  
Hashim Abdul Razak ◽  
Khaled Ghaedi ◽  
Haider Hamad Ghayeb
Keyword(s):  
Data Mining ◽  
Exact Solution ◽  
Structural Health Monitoring ◽  
Health Monitoring ◽  
Structural Damage ◽  
Damage Identification ◽  
Optimization Problems ◽  
Engineering Optimization ◽  
Structural Health ◽  
Structural Damage Identification

In recent years, data mining technology has been employed to solve various Structural Health Monitoring (SHM) problems as a comprehensive strategy because of its computational capability. Optimization is one the most important functions in Data mining. In an engineering optimization problem, it is not easy to find an exact solution. In this regard, evolutionary techniques have been applied as a part of procedure of achieving the exact solution. Therefore, various metaheuristic algorithms have been developed to solve a variety of engineering optimization problems in SHM. This study presents the most applicable as well as effective evolutionary techniques used in structural damage identification. To this end, a brief overview of metaheuristic techniques is discussed in this paper. Then the most applicable optimization-based algorithms in structural damage identification are presented, i.e. Particle Swarm Optimization (PSO), Genetic Algorithm (GA), Imperialist Competitive Algorithm (ICA) and Ant Colony Optimization (ACO). Some related examples are also detailed in order to indicate the efficiency of these algorithms.

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