Back to Search
Start Over
Experimental verification of a data-driven algorithm for drive-by bridge condition monitoring.
- Source :
-
Structure & Infrastructure Engineering: Maintenance, Management, Life-Cycle Design & Performance . Jul/Aug2024, Vol. 20 Issue 7/8, p1174-1196. 23p. - Publication Year :
- 2024
-
Abstract
- As the world's transport infrastructure ages, the importance of bridge condition monitoring is becoming increasingly acknowledged. Large-scale deployment of existing inspection and monitoring techniques is infeasible due to cost and logistical challenges. The concept of using sensors located within vehicles for low cost 'drive-by' monitoring has become the focus of much attention in recent years. This paper presents a new data-driven approach for drive-by bridge monitoring. Machine learning techniques are leveraged to allow the influence of vehicle speed to be considered and the Operating Deflection Shape Ratio (ODSR) is presented as an alternative damage-sensitive feature to the commonly used frequency spectrum. Extensive laboratory experiments demonstrate that the method is capable of detecting midspan cracking and seized bearings. A statistical classification approach is adopted to classify damage indicators as either 'damaged' or 'healthy'. Classification accuracy is seen to vary between 65-96% and is similar whether using the frequency spectrum or ODSR. Based on the results of the laboratory testing, it is expected that this approach could be implemented on a large scale to act as an early warning tool for infrastructure owners to identify bridges presenting signs of distress or deterioration. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 15732479
- Volume :
- 20
- Issue :
- 7/8
- Database :
- Academic Search Index
- Journal :
- Structure & Infrastructure Engineering: Maintenance, Management, Life-Cycle Design & Performance
- Publication Type :
- Academic Journal
- Accession number :
- 177318663
- Full Text :
- https://doi.org/10.1080/15732479.2024.2311902