| Title |
An Integrated Data Mining Approach for Overweight Vehicle Identification Using the EnBWIM Measurement System |
| Authors |
박범준(Park, BumJun) ; 방건혁(Bang, GeonHyeok) ; 이재훈(Lee, JaeHoon) ; 허광희(Heo, GwangHee) ; 전승곤(Jeon, SeungGon) |
| DOI |
https://doi.org/10.12652/Ksce.2026.46.5.0395 |
| Keywords |
EnBWIM; 과중 차량 모니터링; 구조물 건전성 모니터링; 데이터 마이닝; RDBMS; 교량 유지 및 안전 관리 EnBWIM; Overweight vehicle monitoring; Structural health monitoring; Data mining; RDBMS; Bridge maintenance and safety |
| Abstract |
In this study, an integrated data mining approach was proposed for the efficient extraction and management of vehicle load information from real-time strain responses measured using an energy-based Enhanced Bridge Weigh-In-Motion (EnBWIM) system. From the continuously acquired raw EnBWIM data, vehicle passage events were detected and valid response segments were selectively extracted. Strain-energy-based analysis was then performed to obtain vehicle management information, including gross vehicle weight, axle loads, axle spacing, and traveling speed. In addition, identification information, such as vehicle license plate number, passage time, and speed, was extracted from raw data independently acquired using a high-speed (HS) camera and a speed gun and subsequently linked with the corresponding EnBWIM measurements. Data obtained from the different measurement devices were synchronized based on time information, enabling the management and identification information associated with the same vehicle to be integrated. Application of the proposed data mining approach to 24-h continuous monitoring data demonstrated that approximately 99 % of the raw data could be reduced by retaining only the essential information required for overweight vehicle management, thereby significantly improving data storage and processing efficiency. Furthermore, a relational database management system (RDBMS) was developed by integrating the extracted management and identification information to systematically store and retrieve the passage history of individual vehicles. The developed database enables integrated management of gross vehicle weight, axle loads, license plate number, passage time, traveling speed, and axle spacing for individual overweight vehicles. The proposed EnBWIM-based integrated data mining and database framework can therefore facilitate the efficient processing of large-scale continuous monitoring data and long-term tracking of overweight vehicle passage histories. The resulting information can also provide a useful basis for bridge maintenance, assessment of cumulative load effects induced by overweight vehicles, and the development of proactive risk management strategies. |