Abstract
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.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
