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http://dx.doi.org/10.12989/sss.2012.9.3.287

Statistics and probability analysis of vehicle overloads on a rigid frame bridge from long-term monitored strains  

Li, Yinghua (School of Civil Engineering and Transportation, State Key Laboratory of Subtropical Building Science, South China University of Technology)
Tang, Liqun (School of Civil Engineering and Transportation, State Key Laboratory of Subtropical Building Science, South China University of Technology)
Liu, Zejia (School of Civil Engineering and Transportation, State Key Laboratory of Subtropical Building Science, South China University of Technology)
Liu, Yiping (School of Civil Engineering and Transportation, State Key Laboratory of Subtropical Building Science, South China University of Technology)
Publication Information
Smart Structures and Systems / v.9, no.3, 2012 , pp. 287-301 More about this Journal
Abstract
It is well known that overloaded vehicles may cause severe damages to bridges, and how to estimate and evaluate the status of the overloaded vehicles passing through bridges become a challenging problem. Therefore, based on the monitored strain data from a structural health monitoring system (SHM) installed on a bridge, a method is recommended to identify and analyze the probability of overloaded vehicles. Overloaded vehicle loads can cause abnormity in the monitored strains, though the abnormal strains may be small in a concrete continuous rigid frame bridge. Firstly, the abnormal strains are identified from the abundant strains in time sequence by taking the advantage of wavelet transform in abnormal signal identification; secondly, the abnormal strains induced by heavy vehicles are picked up by the comparison between the identified abnormal strains and the strain threshold gotten by finite element analysis of the normal heavy vehicle; finally, according to the determined abnormal strains induced by overloaded vehicles, the statistics of the overloaded vehicles passing through the bridge are summarized and the whole probability of the overloaded vehicles is analyzed. The research shows the feasibility of using the monitored strains from a long-term SHM to identify the information of overloaded vehicles passing through a bridge, which can help the traffic department to master the heavy truck information and do the damage analysis of bridges further.
Keywords
bridges; overloaded vehicles; probability analysis; long-term health monitoring; wavelet transform; FEM;
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