Welcome to Francis Academic Press

Academic Journal of Engineering and Technology Science, 2023, 6(3); doi: 10.25236/AJETS.2023.060304.

Research on the Key Points and Breakthrough of the Road and Bridge Test and Detection

Author(s)

Fang Huang, Ruimin Huang, Nana Qian

Corresponding Author:
Fang Huang
Affiliation(s)

Shandong Transport Vocational College, Weifang, Shandong, China

Abstract

With the perfect layout and construction of China's transportation network, the road and bridge construction projects have increased significantly. Therefore, how to control the quality of construction under the background of quantity surge is worth discussing. The key is to strengthen the road and bridge test detection to escort the quality of roads and bridges. This work mainly discussed the problems of road and bridge test detection, then summarized the problems existing in the current test, and finally took it as the core of detection and supervision. Based on problem analysis, this work tried to explore the problem solving path, hoping to effectively improve the quality of road and bridge construction and really push the stable development of transportation construction industry in China.

Keywords

Road and bridge test detection; Key points; Problems; Suggestions

Cite This Paper

Fang Huang, Ruimin Huang, Nana Qian. Research on the Key Points and Breakthrough of the Road and Bridge Test and Detection. Academic Journal of Engineering and Technology Science (2023) Vol. 6, Issue 3: 20-24. https://doi.org/10.25236/AJETS.2023.060304.

References

[1] Verstrynge E., Lacidogna G., Accornero F., & Tomor A. (2021). A review on acoustic emission monitoring for damage detection in masonry structures. Construction and Building Materials, 268, 121089.

[2] Huseynov F., Kim C., Obrien E. J., Brownjohn J. M. W., Hester D., & Chang K. C. (2020). Bridge damage detection using rotation measurements–Experimental validation. Mechanical Systems and Signal Processing, 135, 106380.

[3] Hüthwohl P., Lu R., & Brilakis I. (2019). Multi-classifier for reinforced concrete bridge defects. Automation in Construction, 105, 102824.

[4] Malekjafarian A., Golpayegani F., Moloney C., & Clarke S. (2019). A machine learning approach to bridge-damage detection using responses measured on a passing vehicle. Sensors, 19(18), 4035.

[5] Zheng M., Lei Z., & Zhang K. (2020). Intelligent detection of building cracks based on deep learning. Image and Vision Computing, 103, 103987.