Education, Science, Technology, Innovation and Life
Open Access
Sign In
  1. Home
  2. Journals
  3. Automation and Machine Learning
  4. Vol 5, Issue 2, 2024

Bridge Detection Structure Analysis Software Based on Neural Network Technology

Download as PDF

DOI: 10.23977/autml.2024.050203 | Downloads: 5 | Views: 254

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.

REFERENCES

[1] Guo Caixing, Zhong Weiqiu, Bu Yanwei, Cao Cong, Xue Zhiping. Research on Testing Methods for Old Concrete Bridge Structures in a Certain City [J]. Low temperature building technology, 2024, 46 (3): 61-64
[2] Yang Bingfeng. Research on Bridge Structural Disease Detection and Treatment Measures [J]. Modern Engineering Technology, 2024, 3 (2): 45-48
[3] Fang Zhouquan. Crack detection technology for bridge concrete structure construction based on image analysis [J]. Journal of Jinhua Vocational and Technical College, 2023, 23 (6): 57-62
[4] Yu Faqiang. Research on Steel Structure Bridge Inspection and Evaluation Combined with Finite Element Analysis [J]. Engineering and Construction, 2023, 37 (1): 243-246
[5] Kong Xiaowu, Jiang Siwei, Liao Qingqing. Structural Inspection and Technical Evaluation Analysis of Bridge on the Dam Top of Hydroelectric Power Station [J]. Yunnan Hydroelectric Power, 2023, 39 (9): 142-145
[6] Zhang Q, Barri K, Babanajad S K, et al. Real-time detection of cracks on concrete bridge decks using deep learning in the frequency domain[J]. Engineering, 2021, 7(12): 1786-1796.
[7] Nguyen T Q. A data-driven approach to structural health monitoring of bridge structures based on the discrete model and FFT-deep learning[J]. Journal of Vibration Engineering & Technologies, 2021, 9(8): 1959-1981.
[8] Karimi S, Mirza O. Damage identification in bridge structures: review of available methods and case studies[J]. Australian Journal of Structural Engineering, 2023, 24(2): 89-119.
[9] Sheng X, Zheng W, Zhu Z, et al. Ganjiang Bridge: A high-speed railway long-span cable-stayed bridge laying ballastless tracks[J]. Structural Engineering International, 2021, 31(1): 40-44.
[10] Miyamoto A. Knowledge-based systems for the assessment and management of bridge structures: a review[J]. Journal of Software Engineering and Applications, 2021, 14(10): 505-536.
[11] Dastres R, Soori M. Artificial neural network systems[J]. International Journal of Imaging and Robotics (IJIR), 2021, 21(2): 13-25.
[12] Chen Z. Research on internet security situation awareness prediction technology based on improved RBF neural network algorithm[J]. Journal of Computational and Cognitive Engineering, 2022, 1(3): 103-108.
[13] Wang G, Zhou J. Dynamic robot path planning system using neural network[J]. Journal of Intelligent & Fuzzy Systems, 2021, 40(2): 3055-3063.
[14] Yan X, Weihan W, Chang M. Research on financial assets transaction prediction model based on LSTM neural network[J]. Neural Computing and Applications, 2021, 33(1): 257-270.
[15] Wang Z, Hu J, Min G, et al. Spatial-temporal cellular traffic prediction for 5G and beyond: A graph neural networks-based approach[J]. IEEE Transactions on Industrial Informatics, 2022, 19(4): 5722-5731.

Subscription
E-Mail Alert
Most popular papers
Call for Special Issue
Publication Ethics & OA Statement
Plagiarism Policy
Contact Us
Downloads: 2381
Visits: 92786

Sponsors, Associates, and Links

All published work is licensed under a Creative Commons Attribution 4.0 International License.

Copyright © 2016 - 2031 Clausius Scientific Press Inc. All Rights Reserved.