Journal of Jilin University(Engineering and Technology Edition) ›› 2020, Vol. 50 ›› Issue (2): 565-571.doi: 10.13229/j.cnki.jdxbgxb20190083

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Health detection of bridge structures based on data mining technology

Li-qun WU1(),Liang-liang ZHANG2   

  1. 1.CET-College of Engineering and Technology，Southwest University, Chongqing 400715, China
    2.School of Civil Engineering, Chongqing University, Chongqing 400045, China
  • Received:2019-01-21 Online:2020-03-01 Published:2020-03-08

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Abstract:

In order to improve the health detection results of bridge structures, a health detection model of bridge structures based on data mining technology is designed to solve the problems of large error and poor detection efficiency in the current health detection model. Firstly, the health data of bridge structure is collected by wireless sensor network, and the data is processed by kernel principal component analysis, which eliminates the redundancy of bridge structure health data and reduces the detection scale of bridge structure health. Then, the support vector machine is used to learn the health data of bridge structure. The parameters of bridge structure health detection model determined by particle swarm optimization algorithm are introduced to establish the optimal bridge structure health detection model. Finally, the effectiveness and superiority of the bridge structure health model are tested on the platform of Matlab 2017. The results show that the model achieves high precision bridge structure health. The health detection results show that the time of bridge structure health modeling is reduced, and the efficiency of bridge structure health detection is improved. Moreover, the overall performance of the proposed bridge health detection model is obviously better than that of other bridge health detection models. The proposed model provides an effective tool for bridge structure health research.

Key words: bridge structure, health status, nuclear principal component analysis, redundancy characteristics, detection accuracy, modeling efficiency

CLC Number: 

  • TM933

Fig.1

Wireless sensor network architecture for collecting health data of bridge structures"

Fig.2

Workflow of bridge structure health condition detection model based on data mining"

Table 1

Sample distribution of research objects for bridge structural health monitoring"

类型标签桥梁结构损伤程度损伤指数训练样本数量/个测试样本数量/个
1微损伤<0.05500150
2轻度损伤0.05~0.415050
3中等损伤0.4~0.758020
4严重损伤0.75~1205

Table 2

Characteristic values of nuclear principal component analysis for different bridge structures under health condition"

桥梁结构损伤程度第1元特征值第2元特征值第3元特征值第4元特征值第5元特征值
微损伤8.735.513.842.351.55
轻度损伤9.904.953.762.061.43
中等损伤13.334.052.001.591.08
严重损伤8.795.132.932.581.32

Fig.3

Simulation experimental results"

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