Statistical Pattern Recognition for Railway Bridge Structural Damage Detection

2010 ◽  
Author(s):  
Deshan Shan ◽  
Chunyu Fu ◽  
Qiao Li
Keyword(s):  
Pattern Recognition ◽  
Damage Detection ◽  
Structural Damage ◽  
Statistical Pattern Recognition ◽  
Railway Bridge ◽  
Structural Damage Detection ◽  
Statistical Pattern

The Damage Detection of Pressure Pipe Based on the Statistical Pattern Recognition Theory

2014 ◽  
Vol 955-959 ◽  
pp. 3432-3436
Author(s):  
Su Min Zhao ◽  
We She He ◽  
Shuang Mei Chang ◽  
Yu Qiang Cheng
Keyword(s):  
Pattern Recognition ◽  
Damage Detection ◽  
Structural Damage ◽  
Test Method ◽  
Statistical Pattern Recognition ◽  
Model Parameters ◽  
Pressure Pipe ◽  
Pattern Recognition Theory ◽  
Statistical Pattern ◽  
Recognition Theory

Based on the statistical pattern recognition theory, the AMRA timing analysis methods are used in the article, through the combination of long autoregressive model residuals method and the least squares method the model parameters are estimated, and a system model is established. By using mean control chart method the vibration information and feature of the pressure pipe are extracted and selected, so whether the pressure pipes is damaged can be judged effectively. The simulation results show that structural abnormalities test method of the mean value ,which is Based on the recognition theory of statistical pattern, can accurately diagnose structural damage detection state ,the injury degree and damage location, it has a very strong sensitivity

The application of statistical pattern recognition methods for damage detection to field data

2008 ◽  
Vol 17 (6) ◽  
pp. 065023 ◽  
Author(s):  
A Cheung ◽  
C Cabrera ◽  
P Sarabandi ◽  
K K Nair ◽  
A Kiremidjian ◽  
...  
Keyword(s):  
Pattern Recognition ◽  
Damage Detection ◽  
Field Data ◽  
Statistical Pattern Recognition ◽  
Pattern Recognition Methods ◽  
Statistical Pattern

Structural Damage Detection Using Signal Pattern-Recognition

2008 ◽  
Vol 400-402 ◽  
pp. 465-470 ◽  
Author(s):  
Long Qiao ◽  
Asad Esmaeily ◽  
Hani G. Melhem
Keyword(s):  
Pattern Recognition ◽  
Damage Detection ◽  
Structural Damage ◽  
Experimental Studies ◽  
Least Square ◽  
Structural Damage Detection ◽  
Damage Scenario ◽  
Damage Pattern ◽  
Damage Scenarios ◽  
Unknown Damage

Deterioration significantly affects the structure performance and safety. A signal-based pattern-recognition procedure is applied for structural damage detection with a limited number of input/output signals. The method is based on extracting and selecting the sensitive features of the structure response to form a unique pattern for any particular damage scenario, and recognizing the unknown damage pattern against the known database to identify the damage location and level (severity). In this study, two types of transformation algorithms are implemented separately for feature extraction: (1) Continuous Wavelet Transform (CWT); and (2) Wavelet Packet Transform (WPT). Three pattern-matching algorithms are also implemented separately for pattern recognition: (1) correlation, (2) least square distance, and (3) Cosh spectral distance. To demonstrate the validity and accuracy of the procedure, experimental studies are conducted on a simple three-story steel structure. The results show that the features of the signal for different damage scenarios can be uniquely identified by these transformations, and correlation algorithms can best perform pattern recognition to identify the unknown damage pattern. The proposed method can also be used to possibly detect the type of damage. It is suitable for structural health monitoring, especially for online monitoring applications.

Structural Damage Detection and Classification Algorithm Based on Artificial Immune Pattern Recognition

2014 ◽  
Vol 945-949 ◽  
pp. 1265-1269
Author(s):  
Dong Wei Zhang ◽  
Yong Ming Mao ◽  
Feng Long Kan ◽  
Nan Chen
Keyword(s):  
Pattern Recognition ◽  
Damage Detection ◽  
Structural Damage ◽  
Damage Model ◽  
Classification Algorithm ◽  
Immune Recognition ◽  
Structural Damage Detection ◽  
Classification Problems ◽  
Artificial Immune ◽  
Classification Rate

This paper studies the structural damage detection and classification problems by using the artificial immune system which has the extremely powerful capabilities of adaptive and the bionic principle between learning and memory. We proposed an artificial immune pattern recognition and structural detection classification algorithm through imitating the immune recognition and learning mechanism. With the structure of benchmark, the damage detection and classification are tested. The simulation results show the classification rate is very well. The algorithm based on the immune learning and evolution can produce the high quality memory cells which effectively identify all kinds of structural damage model.

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