Application of Internet of Things Technology and Convolutional Neural Network Model in Bridge Crack Detection | Zendy

Liyan Zhang | Zendy; Guanchen Zhou | Zendy; Yang Han | Zendy; Honglei Lin | Zendy; Yuying Wu | Zendy

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Application of Internet of Things Technology and Convolutional Neural Network Model in Bridge Crack Detection

Author(s) -

Liyan Zhang,

Guanchen Zhou,

Yang Han,

Honglei Lin,

Yuying Wu

Publication year - 2018

Publication title -

ieee access

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.587

H-Index - 127

ISSN - 2169-3536

DOI - 10.1109/access.2018.2855144

Subject(s) - aerospace , bioengineering , communication, networking and broadcast technologies , components, circuits, devices and systems , computing and processing , engineered materials, dielectrics and plasmas , engineering profession , fields, waves and electromagnetics , general topics for engineers , geoscience , nuclear engineering , photonics and electrooptics , power, energy and industry applications , robotics and control systems , signal processing and analysis , transportation

With the development of information technology, the Internet of Things (IoT) has the characteristics of strong permeability, large use of action, and good comprehensive benefits. It promotes the development of the IoT technology in the detection of structural engineering. It is conducive to the development of intelligent, refined, and networked structures. Crack is the most common threat to the safety of bridges. Historical data show that the safety accidents caused by cracks account for more than 90% of the total bridge disasters. After a long period of engineering practice and rigorous theoretical analysis, it was found that 0.3 mm is the maximum allowable for bridge cracks. If the width exceeds the limit, the integrity of the bridge will be destroyed, and even a collapse accident will occur. Therefore, it is very important to identify cracks in bridge structure effectively and provide effective information for structural disaster reduction projects in time. Based on the structure of the IoT and the structural characteristics of the bridge engineering, this paper analyzed the practical application value of the IoT technology in the crack identification of bridge structures and established a bridge structure health monitoring system based on the IoT technology. On this basis, this paper also studied a digital and intelligent bridge crack detection method to improve the efficiency of bridge safety diagnosis and reduced the risk factor. First, the collected bridge crack photographs were preprocessed, the bridge crack convolution neural network classification model was established, and the model was simulated and trained using MATLAB. The bridge crack classification was obtained. The simulation results showed that the overall accuracy rate was greater than 90%.

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