Modal Parameter Identification of Bridge based on Large Scale Data Sets

Abstract

The main objective of this paper was to carry out an effective and meticulous long term state identification of cable stayed bridge, from a large amount of data collected from long span cable stayed bridge. In order to achieve the above objective, data visualization techniques were employed, because it can provide a quick and effective data analysis due to its graphical interface of data visualization. For this purpose a long span cable stayed bridge, having a main span of 1088m was selected as a case study. Firstly the data was collected from long span bridge, then based on data visualization outcome, Data Driven Stochastic Subspace Identification (DATA-SSI) technique has been employed to identify the modal parameters such as modal frequencies and damping ratios by plotting its stable diagrams. The results showed that the proposed method was effective in attaining its goals and can endows better results especially for long term continuous data and can prove to be a valuable tool in bridge health monitoring.