Smart Structures and Systems
Volume 9, Number 3, 2012, pages 287-301
DOI: 10.12989/sss.2012.9.3.287
Statistics and probability analysis of vehicle overloads on a rigid frame bridge from long-term monitored strains
Yinghua Li, Liqun Tang, Zejia Liu and Yiping Liu
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 longterm
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.
Key Words
bridges; overloaded vehicles; probability analysis; long-term health monitoring; wavelet transform; FEM
Address
Yinghua Li, Liqun Tang, Zejia Liu and Yiping Liu : School of Civil Engineering and Transportation, State Key Laboratory of Subtropical Building Science, South China University of Technology, Guangzhou, China. 510640