Structural damage detection of a roof truss utilizing measured vibration data following the Bayesian model updating method

Long span roof trusses, for their structural efficiency, are very popular in Hong Kong and worldwide. Collapse of this kind of structures is cataclysmic owing to the large area of coverage (e.g., the collapse of roof truss of the Sport Hall at City University of Hong Kong, Hong Kong SAR, on 20 May 2016). It is believed that structural damage reduces the corresponding member stiffness of the system. As a result, the dynamic characteristics, such as natural frequencies and mode shapes, of the system will be altered. The main purpose of this study is to test the feasibility of using measured vibration data of the roof truss for its damage detection by solving the inverse problem. This model updating problem for structural damage detection is sensitive to the level of modeling error and measurement noise. In this study, the Bayesian approach will be followed to explicitly address the uncertainty problem. Instead of calculating the stiffness value of various structural members, the method aims in estimating the updated probability density function (PDF) of stiffness parameters. The method was verified through measured data from a roof truss model under laboratory conditions. The list of limitations and difficulties of this approach are discussed in this study, and possible solutions for future research are suggested.