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논문 기본 정보
- 자료유형
- 학술저널
- 저자정보
- 저널정보
- 국제구조공학회 Smart Structures and Systems, An International Journal Smart Structures and Systems, An International Journal Vol.24 No.6
- 발행연도
- 2019.1
- 수록면
- 723 - 732 (10page)
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초록· 키워드
오류제보하기
Globally road transport networks are subjected to continuous levels of stress from increasing loading and environmental effects. As the most popular mean of transport in the UK the condition of this civil infrastructure is a key indicator of economic growth and productivity. Structural Health Monitoring (SHM) systems can provide a valuable insight to the true condition of our aging infrastructure. In particular, monitoring of the displacement of a bridge structure under live loading can provide an accurate descriptor of bridge condition. In the past B WIM systems have been used to collect traffic data and hence provide an indicator of bridge condition, however the use of such systems can be restricted by bridge type, assess issues and cost limitations. This research provides a non contact low cost AI based solution for vehicle classification and associated bridge displacement using computer vision methods. Convolutional neural networks (CNNs) have been adapted to develop the QUBYOLO vehicle classification method from recorded traffic images. This vehicle classification was then accurately related to the corresponding bridge response obtained under live loading using non contact methods. The successful identification of multiple vehicle types during field testing has shown that QUBYOLO is suitable for the fine grained vehicle classification required to identify applied load to a bridge structure. The process of displacement analysis and vehicle classification for the purposes of load identification which was used in this research adds to the body of knowledge on the monitoring of existing bridge structures, particularly long span bridges, and establishes the significant potential of computer vision and Deep Learning to provide dependable results on the real response of our infrastructure to existing and potential increased loading.
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참고문헌 (34)
참고문헌 신청
ACSE / Report card for America's infrastructure
Ahmad Arinaldi / 2018 / Detection and classification of vehicles for traffic video analytics / Procedia Computer Science 144:259~268
Cavaioni, M. / Deep learning series: Convolutional neural networks - machine learning bites - medium / Medium/Machine Learning
Chuan-Zhi Dong / 2018 / Marker-free monitoring of the grandstand structures and modal identification using computer vision methods / Structural Health Monitoring 18(5-6):1491~1509
Maria Q. Feng / 2015 / Nontarget Vision Sensor for Remote Measurement of Bridge Dynamic Response / Journal of Bridge Engineering 20(12):04015023
Ahmad Arinaldi / 2018 / Detection and classification of vehicles for traffic video analytics / Procedia Computer Science 144:259~268
Cavaioni, M. / Deep learning series: Convolutional neural networks - machine learning bites - medium / Medium/Machine Learning
Chuan-Zhi Dong / 2018 / Marker-free monitoring of the grandstand structures and modal identification using computer vision methods / Structural Health Monitoring 18(5-6):1491~1509
Maria Q. Feng / 2015 / Nontarget Vision Sensor for Remote Measurement of Bridge Dynamic Response / Journal of Bridge Engineering 20(12):04015023
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