Methodologies for predicting natural frequency variation of a suspension 1 bridge

12 In vibration-based structural health monitoring, changes in the natural frequency of a structure 13 are used to identify changes in the structural conditions due to damage and deterioration. 14 However, natural frequency values also vary with changes in environmental factors such as 15 temperature and wind. Therefore, it is important to differentiate between the effects due to 16 environmental variations and those resulting from structural damage. In this paper, this task is 17 accomplished by predicting the natural frequency of a structure using measurements of 18 environmental conditions. Five methodologies-multiple linear regression, artificial neural 19 networks, support vector regression, regression tree and random forest-are implemented to 20 predict the natural frequencies of the Tamar Suspension Bridge (UK) using measurements 21 taken from three years of continuous monitoring. The effects of environmental factors and 22 traffic loading on natural frequencies are also evaluated by measuring the relative importance 23 of input variables in regression analysis. Results show that support vector regression and 24 random forest are the most suitable methods for predicting variations in natural frequencies. 25 In addition, traffic loading and temperature are found to be two important parameters that 26 need to be measured. Results show potential for application to continuously monitored 27 structures that have complex relationships between natural frequencies and parameters such as 28 loading and environmental factors.