Dynamic reliability prediction of bridges based on nonlinear MGPF algorithm and SHM data

作者：Liu, Yuefei; Fan, Xueping^*

来源：Construction and Building Materials, 2017, 155: 1242-1249.

DOI：10.1016/j.conbuildmat.2017.07.096

• Structural health monitoring
• Nonlinear dynamic model
• Mixed Gaussian particle filtering algorithm
• FOSM

Bridge health monitoring system, produces much monitored data during the long-term service periods. How to properly handle with these data is one of the main difficulties in the field of Structural Health Monitoring (SHM). To predict the structural reliability with these data, the objectives of this paper are to present: (1) a nonlinear dynamic model, (2) nonlinear Mixed Gaussian Particle Filtering (MGPF) algorithm, (3) a dynamic reliability prediction approach combining nonlinear MGPF algorithm and SHM data with first order second moment (FOSM) method. And the monitored extreme stress data of an actual bridge is provided to illustrate the application and feasibility of the proposed models and methods.

• 出版日期2017-11-30
• 单位兰州大学