Application and comparison of GRNN, BPNN and RBFNN in the prediction of suspender frequency and tension on arch bridge

Abstract

The prediction of suspender frequency and tension is difficult to solve due to the non-linear nature of suspender parameters. A method of predicting suspender frequency and tension using the generalized regression neural network (GRNN) model was proposed in this paper. It is necessary to select some suspender parameters as inputs into the model to solve the non-linear nature problem of the suspender parameters, such as length, mass unit per length, bending stiffness, fundamental frequency as well as tension, and to select the suspender frequency or tension as output. To consider the effect of different boundary constraints, analytical expressions of suspender parameters based on the singular perturbation method are derived and applied to train the models. Two different types of neural network models: back propagation neural network (BPNN) and radial basis function neural network (RBFNN), are also used to predict suspender frequency and tension to compare with the GRNN model. Datasets consist of measurements and literature samples are used to verify the models. Furthermore, R², MAE, and RMSE are used to compare the performance of the models. The results showed that the application of GRNN achieves higher accuracy in predicting suspender frequency and tension compared to BPNN and RBFNN.