Title

Uncertainty And Reliability Analysis Using Monitoring Data And Artificial Neural Network (Ann) Calibration

Abstract

Probabilistic techniques in engineering problems provide a deeper understanding of the aleatory and epistemic uncertainties inherent to the structures being analyzed. Complex engineering structures are usually analyzed with finite element techniques to incorporate all critical details with geometric representation. The prediction of structural reliability based on a pre-defined limit state can be obtained with a finite element model and can be updated using Bayesian methods with the monitoring data. Another common approach is to calibrate a finite element model with the monitoring data by minimizing the error between the analysis and the measurements, which requires more time and user interaction. The objective of this paper is to explore the comparison of the model responses and predictions between these two approaches where uncertainties are incorporated in a different manner. For this study, a test set-up which is a simplified series-parallel physical model with four structural elements (Double-H-Frame-DHF) is designed and extensively instrumented with various sensors, and monitored over time with different structural boundary conditions. A large number of simulations using the finite element model are performed under uncertainties associated with material properties, geometry, loading and boundary conditions. The boundary conditions are changed gradually and the two approaches are executed to obtain the reliability distribution at each structural state and also to predict future performance. The findings from the two approaches are compared. © 2010 Taylor & Francis Group, London.

Publication Date

12-1-2010

Publication Title

Bridge Maintenance, Safety, Management and Life-Cycle Optimization - Proceedings of the 5th International Conference on Bridge Maintenance, Safety and Management

Number of Pages

738-746

Document Type

Article; Proceedings Paper

Personal Identifier

scopus

Socpus ID

84856761696 (Scopus)

Source API URL

https://api.elsevier.com/content/abstract/scopus_id/84856761696

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