[This article belongs to Volume - 53, Issue - 03]
Gongcheng Kexue Yu Jishu/Advanced Engineering Science
Journal ID : AES-16-10-2021-62
Total View : 1

Title : Research on Neural Network Generalization of Cable Force Vibration Measurement
GAI Tongtong, ZENG Sen, YU Dehu, YANG Shujuan, SUN Baodi,

Abstract :

The stress state of the cable is related to the safety of the cable system bridge, and the cable force value is an important index to measure the mechanical states of the cable. At present, the difficulty of determining the cable boundary conditions is an important factor affecting the accuracy of the cable force identification results. The ANSYS was used to numerically simulate the cable vibration, and the reliability of the modeling method was verified by the existing cable force calculation formula and the simulation data was generated. Then taken cable length, line density, bending stiffness, first-order frequency, second-order frequency, and third-order frequency as the input parameters, and used cable force as output parameter combined with vibration simulation data to establish BP neural network and generalized regression neural network cable force prediction model. Two neural network cable force prediction models and the existing cable force calculation formula were applied to actual projects for comparison and verification. The results showed that the neural network structure of the BP neural network cable force prediction model was 6–13–13–1, the activation functions between the input layer and the hidden layer 1, the hidden layer 1 and the hidden layer 2, the hidden layer 2 and the output layer were tansig, tansig, purelin, the training algorithm was the L–M optimization algorithm trainlm, the learning rate was 0.1, the number of network iterations was 1 000, the display interval was 100, the mean square error was 0.001, the prediction effect of the cable force prediction model was good, but there was room for further optimization. The best spread value of the generalized regression neural network cable force prediction model was 0.002 15, the prediction effect of the cable force prediction model was better than that of the BP neural network and the existing cable force calculation formula, and the forecast error was basically controlled within 5%. Utilizing the generalized regression neural network to predict the cable force of the bridge can avoid the influence of the judgment error of the cable boundary condition on the accuracy of the cable force recognition result, and improve the accuracy of the cable force recognition, which has a good engineering application value.