Abstract
In this study, the mechanism of the effect of temperature on structural frequency is investigated by integrating correlation analysis, numerical simulation, and neural network techniques. First, the different spatial–temporal influence patterns of the temperature field on frequency are observed using correlation analysis based on the long-term monitoring of an arch bridge. Subsequently, a numerical simulation is performed to quantitatively analyze the effect of temperature on frequency in terms of internal force, geometric size, elastic modulus, and boundary condition. It is observed that an unknown factor—except for the elastic modulus—leads to variation in the frequency of the beam mode. The spatial effect of the temperature field is separated by a genetic-algorithm-optimized backpropagation neural network. It is inferred that the frequency of the beam mode is influenced in terms of the boundary condition and the elastic modulus because of temperature, whereas the frequency of the arch mode is only influenced by the changing elastic modulus.
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