Application of Artificial Intelligence Technology in Bridge Construction and Maintenance
DOI:
https://doi.org/10.54097/cbm3py13Keywords:
Artificial Intelligence; bridge construction; bridge maintenance.Abstract
Artificial intelligence (AI) can extract valuable information from analyzing and learning among large amounts of data from bridge. In recent years, the researches and applications of AI in the field of bridge construction and maintenance are gradually increasing with the development of AI. This paper aims to summarize the application of AI in bridge construction and maintenance in recent years. Results show that AI technology can simulate and predict materials and construction scheme and promote the development of material science. In terms of intelligent detection, AI enables automated, efficient, and precise bridge inspection through technologies like visual machines and deep learning. Furthermore, in the realm of intelligent disaster prevention, early warning systems for potential bridge disasters can be achieved via big data analysis and predictive models. In the future, the design, construction and maintenance of Bridges will be increasingly combined with AI technologies, thus making the construction and management of Bridges more information-based and intelligent.
Downloads
References
Zhao Tianqi, Gou Hongye, Chen Xuanying, et al. Research progress of bridge informatization and Intelligent Bridge in 2020. Journal of Civil and Environmental Engineering, 2021, 43(S1): 268-279.
Xia Zhongli. Problems existing in Road and bridge maintenance and discussion on preventive measures. Chinese Science and Technology Journal Database ‘full-text edition’ Engineering Technology, 2016, (12): 00012.
Li Ruijun, Niu Chaoran. Application of smart materials in bridge engineering. Municipal Technology, 2010, (4): 145-147.
Jia Wenjie, Ding Hanshan. Research on intelligent prestressed bridge system based on shape memory alloy materials. Modern Transportation Technology, 2007, 4(6): 45-48.
Mitchell T. Machine learning and data mining. Communications of the ACM, 1999, 42(11): 30-36.
Xue D, Balachandran P V, Hogden J, et al. Accelerated search for materials with targeted properties by adaptive design. Nature Communications, 2016, 7: 11241.
Liang D, Zheng Y, Fang C, et al. Shape memory alloy (SMA) cable-controlled sliding bearings: Development, testing and system behavior. Smart Materials and Structures, 2020, 29 (8): 085006.
Perry B J, Guo Y, Atadero R, et al. Streamlined bridge inspection system utilizing unmanned aerial vehicles (UAVs) and machine learning. Measurement, 2020, 164: 108048.
Zhou Y, Pei Y, Li Z, et al. Vehicle weight identification system for spatiotemporal load distribution on bridges based on non-contact machine vision technology and deep learning algorithms. Measurement, 2020, 159: 107801.
Yuan Wancheng, Wang Sijie, Li Huaifeng, et al. Development of seismic intelligence and toughness of Bridges. China Journal of Highway and Transportation, 2021, 34(2): 98.
Banihabib M E, Jurik L, Kazemi M S, et al. A hybrid intelligence model for the prediction of the peak flow of debris floods. Water, 2020, 12(8): 2246.
Downloads
Published
Issue
Section
License
Copyright (c) 2023 Highlights in Science, Engineering and Technology

This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.







