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  4. Vol 5, Issue 2, 2024

Bridge Detection Structure Analysis Software Based on Neural Network Technology

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DOI: 10.23977/autml.2024.050203 | Downloads: 0 | Views: 26

Author(s)

Wenjie Yang 1,2, Gongxing Yan 3, Wangming Wu 4, Xiaoping Zou 1, Yuhu Sun 4

Affiliation(s)

1 Sichuan Jinghengxin Construction Engineering Testing Co., Ltd, Luzhou, 646000, China
2 School of Materials Science and Engineering, Wuhan Institute of Technology, Wuhan, 430205, China
3 School of Intelligent Construction, Luzhou Vocational and Technical College, Luzhou, 646000, China
4 Aneng Third Bureau Chengdu Engineering Quality Testing Co., Ltd, Chengdu, 611130, China

Corresponding Author

Gongxing Yan

ABSTRACT

Bridges are the transportation infrastructure of cities, and their safety and health status are related to people's travel safety and the operational efficiency of cities. However, over time, traditional bridge detection methods have shown significant limitations, including low efficiency and insufficient accuracy. This article proposed a bridge detection structure analysis software based on neural network technology. The software adopted Convolutional Neural Network (CNN) as the core algorithm, utilizing its high accuracy and comprehensiveness in image recognition to automatically extract multi-level features in bridge images and effectively identify damage details such as cracks and corrosion. The software design considered modularity and scalability, including key aspects such as data acquisition and preprocessing, neural network model selection and training, user interface design, and performance optimization. The experimental results showed that the developed software performed well in bridge defect detection tasks, with a detection accuracy of up to 99% and a detection time of no more than 785 milliseconds, demonstrating the ability to respond quickly. The CNN model had the lowest missed detection rate, only 0.16%, while the detection coverage reached 99.75%, significantly better than Recurrent Neural Network (RNN) and Generative Adversarial Network (GAN) models. The application cases of the software in actual bridge detection further verified its efficiency and accuracy.

KEYWORDS

Bridge Inspection; Convolutional Neural Networks; Detection Accuracy; Detection Time

CITE THIS PAPER

Wenjie Yang, Gongxing Yan, Wangming Wu, Xiaoping Zou, Yuhu Sun, Bridge Detection Structure Analysis Software Based on Neural Network Technology. Automation and Machine Learning (2024) Vol. 5: 17-24. DOI: http://dx.doi.org/10.23977/autml.2024.050203.

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