Natural Frequency and Displacement Ratio based Probabilistic Damage Identification for Bridges using FE Model Update

Abstract

This paper proposes a natural frequency and displacement ratio-based probabilistic damage identification method for bridges using the finite element (FE) model update. When the damage location is known, it can be detected from an appropriate damage-sensitive feature (DSF). However, damaged components are seldom known before inspections. This makes it difficult to find an appropriate DSF and damage identification is sometimes challenging. This paper aims to propose a method to solve this issue by integrating multiple DSFs, natural frequencies and displacement ratio, as a decision-level data fusion approach. They are complementary in terms of sensitivity to damage. In addition, probability density functions (PDFs) of structural parameters are estimated from PDFs of observed DSFs through the FE model update to consider errors and uncertainties in measurement data. An in-house model bridge experiment is carried out to investigate the feasibility. The results demonstrated that the two kinds of damages in a bearing and girder reproduced in the experiment were successfully identified without false positives even when these damages simultaneously occurred.