Bayesian network-based modal frequency–multiple environmental factors pattern recognition for the Xinguang Bridge using long-term monitoring data

Abstract

Modal frequency is an important indicator reflecting the health status of a structure. Numerous investigations have shown that its fluctuations are related to the changing environmental factors. Thus, modelling the modal frequency–multiple environmental factors relation is essential for making reliable inference in structural health monitoring. In this study, the Bayesian network (BN)-based algorithm is developed for recognizing the pattern between modal frequency and multiple environmental factors. Different candidates of network structure of the BN are proposed to describe the possible statistical relations of different variables. In the BN-based pattern recognition, the learning phase conducts uncertainty quantification in both parameter and model levels; and the prediction phase makes inference under complete and incomplete observed information. Based on the long-term monitoring data, the most plausible network structure is selected, and its associated parameters are identified. The developed algorithm is then utilized for analyzing the long-term monitoring data (modal frequencies, temperature, humidity, wind speed and traffic volume) of the Xinguang Bridge (a 782-m three-span half-through arch bridge). It turns out that the selected network structure properly captures the pattern of modal frequency–multiple environmental factors.