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Theory and case study of vehicle load identification based on BWIM of steel truss bridge
Author(s) -
Chen Weizhen,
Yang Guang
Publication year - 2013
Publication title -
stahlbau
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.268
H-Index - 19
eISSN - 1437-1049
pISSN - 0038-9145
DOI - 10.1002/stab.201320025
Subject(s) - structural engineering , axle , engineering , axle load , truss , structural load , bridge (graph theory) , deck , truss bridge , influence line , stress (linguistics) , automotive engineering , design load , medicine , linguistics , philosophy
In China recent accidents involving sudden collapses of bridges have aroused great concern about real traffic loads and the loadbearing capacities of existing bridges. However, the proper management of such bridges relies on accurate information on bridge operating loads, which is essential for assessing correctly the operation and safety of bridges. Therefore, this paper proposes an integrated load identification system for operating traffic which is based on the characteristics of a steel truss bridge, i.e. the finite distribution of stress influence lines of suspender and orthotropic steel deck, and employs the BWIM (bridge weigh‐in‐motion) technique to establish the functional relationship between vehicle load and stress history as well as the functional relationship between axle load and stress history. To verify the system, a stress monitoring case study was carried out on two steel truss bridges under both controlled and normal traffic conditions. In the case of controlled traffic, monitored information is used to back‐calculate vehicle load and axle load. The load information is then compared with a synchronous video recording of the traffic to check back‐calculated data about vehicle weight, vehicle speed, wheel weight and the deviation between lateral distribution and actual condition, coupling the relationship between vehicle load and corresponding axle load in order to assure the validity of the identification system established. In the case of normal traffic, monitored stress data are used to back‐calculate vehicle load, axle load, vehicle speed and wheel lateral distribution, which are further analysed to work out a probability distribution so that it can directly help assess the real load capacity and fatigue life of the bridges.