Dual physically-driven and data-driven method for reconstructing internal response of bridge

Abstract

Disclosed is a dual physically-driven and data-driven method for reconstructing internal response of a bridge. The method includes: obtaining acceleration response by an acceleration sensor under an action of an unknown load of the bridge; embedding a physical logic into a neural network based on a frequency response function; putting a physical formula and corresponding boundary conditions and initial conditions into a loss function as penalty terms, and limiting a space of a feasible solution accordingly; and training a neural network model, and predicting acceleration of an unknown point by inputting an acceleration response set of a known point obtained by the sensor into the network. The formula is solved by converting direct solving of a control formula into optimization of the loss function, such that the problems that the internal response of the bridge is difficult to measure and excessively depends measured data can be effectively solved, and accuracy and robustness of internal response prediction of the bridge can be improved.