Stochastic process model of vehicle loads based on structural health monitoring data and maximum prediction of general renewal processes

摘要

Vehicle loads are the most important live load on bridges. It's significant to study the maximum vehicle load in serving period for bridge design, maintenance and safety-evaluation. Stochastic process, such as Possion process or Erlang process, is a powerful model for understanding vehicle loads. While Possion process or Erlang process is only fit for vehicle load acting on one specific bridge, but not fit for the complex vehicle load cases. In this paper, the general gamma process model are used to calculate vehicle load maximum CDF for both loose status and dense status, and maximum CDF prediction method for general renewal processes are put forward to study vehicle load maximum and it's CDF. The numerical results show good agreement with the Yangtze River bridge health monitoring in-field data, which prove the suitability and practicability of the numerical simulation, and provide a reference for the actual project.