Probabilistic damage identification for bridges using multiple damage-sensitive features and FE model update

Abstract

A specific damage-sensitive feature (DSF) is usually sensitive to damage in a certain area, and the specific location of damage in a specific area can be detected by selecting appropriate DSFs. 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 natural frequency and displacement ratio-based probabilistic damage identification method for bridges using the finite element (FE) model update to solve this issue. The displacement ratio measured at two positions on the bridge is proposed as a new DSF. It is a useful DSF because it is independent of external loads. By integrating displacement ratios and natural frequencies, that is, multiple DSFs, as a decision-level data fusion approach, this paper identifies damaged components without choosing an appropriate DSF beforehand. In addition, probability density functions (PDFs) of structural parameters are estimated from PDFs of derived DSFs through the FE model update to consider errors and uncertainties in measurements. 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.