China Safety Science Journal ›› 2018, Vol. 28 ›› Issue (9): 98-102.doi: 10.16265/j.cnki.issn1003-3033.2018.09.017

• Safety Science of Engineering and Technology • Previous Articles     Next Articles

Dynamic grading clustering method based model for evaluation of service state of bridge structure

LIU Chaofeng1,2, CAO Chunbo1, LIU Caiwei3, ZHAO Shaowei1,2, LIU Zhipeng4   

  1. 1 School of Civil and Transportation Engineering,Hebei University of Technology,Tianjin 300401,China
    2 Research Center of Civil Engineering Technology of Hebei Province,Hebei University of Technology, Tianjin 300401, China
    3 College of Civil Engineering, Qingdao University of Technology, Qingdao Shandong 266033, China
    4 Tianjin Highway Engineering Corporation, Tianjin 300041, China
  • Received:2018-06-10 Revised:2018-07-30 Online:2018-09-28 Published:2020-09-28

Abstract: In order to accurately and quickly assess the structural state of existing service bridges, combined with the inspection specifications and structural characteristics of bridge projects, a quantitative index system for the service status of reinforced concrete bridges was constructed from the four dimensions of geometric characteristics, material properties, environmental conditions and action types. Based on the DGCM and sorting method, a dynamic cluster evaluation model for the service statuses of bridge structures was established. The model was used to evaluate the statuses of 11 bridges and a comparison was made between the results and those by other methods. The results show that the results by bridge classification under the optimal number of clusters are completely consistent with the results by the other two methods, and that compared with the existing normative standard methods, it can more accurately and scientifically assess the actual service status of bridges, guide prioritizing of bridge maintenance and resource allocation activities, and provide a basis for developing a reasonable bridge management strategy.

Key words: bridge, service status, dynamic grading clustering method(DGCM), assessment method, natural frequency

CLC Number: