Structural damage assessment using principal component analysis

2004 ◽  
Author(s):  
Chih-Chen Chang ◽  
K. W. Sze ◽  
Zhi Sun
Keyword(s):  
Principal Component Analysis ◽  
Damage Assessment ◽  
Structural Damage ◽  
Principal Component ◽  
Component Analysis

Research for Structural Damage Identification Method Based on Stable Time Series and Principal Component Analysis

2014 ◽  
Vol 578-579 ◽  
pp. 1020-1023
Author(s):  
Jing Zhou Lu ◽  
Jia Chen Wang ◽  
Xu Zhu
Keyword(s):  
Principal Component Analysis ◽  
Time Series ◽  
Damage Detection ◽  
Structural Damage ◽  
Computing Time ◽  
Principal Component ◽  
Component Analysis ◽  
Identification Accuracy ◽  
Frame Structure ◽  
Ar Model

In this paper, we introduce a set of techniques for time series analysis based on principal component analysis (PCA). Firstly, the autoregressive (AR) model is established using acceleration response data, and the root mean squared error (RMSE) of AR model is calculated based on PCA. Then a new damage sensitive feature (DSF) based on the AR coefficients is presented. To test the efficacy of the damage detection and localization methodologies, the algorithm has been tested on the analytical and experimental results of a three-story frame structure model of the Los Alamos National Laboratory. The result of the damage detection indicates that the algorithm is able to identify and localize minor to severe damage as defined for the structure. It shows that the suggested method can lead to less amount of computing time, high suitability and identification accuracy.

scholarly journals Study on Structural Characteristics of Composite Smart Grille Based on Principal Component Analysis

2022 ◽  
Vol 2022 ◽  
pp. 1-11
Author(s):  
Kong Fanxiao ◽  
Yao Huazhong ◽  
Xie Weidong
Keyword(s):  
Principal Component Analysis ◽  
Structural Damage ◽  
Nearest Neighbor ◽  
Structural Characteristics ◽  
Principal Component ◽  
Composite Plates ◽  
Component Analysis ◽  
Triangular Grid ◽  
Grid Structure ◽  
Good Ability

In recent years, many scholars have conducted in-depth and extensive research on the mechanical properties, preparation methods, and structural optimization of grid structural materials. In this paper, the structural characteristics of composite intelligent grid are studied by combining theoretical analysis with experiments. According to the existing conditions in the laboratory, the equilateral triangular grid structure experimental pieces were prepared. In this paper, principal component analysis combined with nearest neighbor method was used to detect the damage of composite plates. On this basis, the multiobjective robustness optimization of the structure is carried out based on artificial intelligence algorithm, which makes the structure quality and its sensitivity to uncertain parameters lower. Particle swarm optimization (PSO) is used in neural network training. The damage characteristics of different grid structures, different impact positions, and different impact energies were studied. The results show that the structural damage types, areas, and propagation characteristics are very different when the structure is impacted at different positions, which verifies that the grid structure has a good ability to limit the damage diffusion and shows that the grid structure has a good ability to resist damage.

scholarly journals Principal Component Analysis Method with Space and Time Windows for Damage Detection

Sensors ◽  
2019 ◽  
Vol 19 (11) ◽  
pp. 2521 ◽  
Author(s):  
Ge Zhang ◽  
Liqun Tang ◽  
Licheng Zhou ◽  
Zejia Liu ◽  
Yiping Liu ◽  
...  
Keyword(s):  
Principal Component Analysis ◽  
Damage Detection ◽  
Structural Damage ◽  
Time Windows ◽  
Principal Component ◽  
Element Model ◽  
Component Analysis ◽  
Previous Method ◽  
Beam Model ◽  
Space And Time

Long-term structural health monitoring (SHM) has become an important tool to ensure the safety of infrastructures. However, determining methods to extract valuable information from large amounts of data from SHM systems for effective identification of damage still remains a major challenge. This paper provides a novel effective method for structural damage detection by introduction of space and time windows in the traditional principal component analysis (PCA) technique. Numerical results with a planar beam model demonstrate that, due to the presence of space and time windows, the proposed double-window PCA method (DWPCA) has a higher sensitivity for damage identification than the previous method moving PCA (MPCA), which combines only time windows with PCA. Further studies indicate that the developed approach, as compared to the MPCA method, has a higher resolution in localizing damage by space windows and also in quantitative evaluation of damage severity. Finally, a finite-element model of a practical bridge is used to prove that the proposed DWPCA method has greater sensitivity for damage detection than traditional methods and potential for applications in practical engineering.

Structural Damage Assessment using an Artificial Immune System

2015 ◽  
pp. 86-102
Author(s):  
Maribel Anaya Vejar ◽  
Diego Alexander Tibaduiza Burgos ◽  
Francesc Pozo
Keyword(s):  
Principal Component Analysis ◽  
Immune System ◽  
Damage Assessment ◽  
Structural Damage ◽  
Artificial Immune System ◽  
Principal Component ◽  
Second Step ◽  
Artificial Immune ◽  
Current State ◽  
Using Data

Structural damage assessment methodologies allow providing knowledge about the current state of the structure. This information is important because allows to avoid possible accidents and perform maintenance tasks in the structure. This chapter proposes the use of an artificial immune system to detect and classify damages in structures by using data from a multi-actuator piezoelectric system that is working in several actuation phases. In a first step of the methodology, principal component analysis (PCA) is used to build a baseline model by using the collected data. In a second step, the same experiments under similar conditions are performed with the structure in different states (damaged or not). These data are projected into the different baseline models for each actuator, in order to obtain the damages indices and build the antigens. The antigens are compared with the antibodies by using an affinity function and the result of this process allows detecting and classifying damages.

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