Research on Cable Tension Prediction Based on Neural Network - Hongbin Zhang; Weihao Hu (Buildings, 2024)
Conventional methods for calculating tension currently suffer from an excessive simplification of boundary conditions and a vague definition of effective cable length, both of which cause inaccurate cable tension calculations. Therefore, this study utilizes bridge field data to establish a BP neural network for tension prediction, with design cable length, line density, and frequency as the input parameters and with cable tension as the output parameter. After disregarding the selection of effective cable length and innovatively integrating the particle swarm optimization–back propagation (PSO-BP) neural network for tension prediction, it is found that the MAPE between the predicted results of the BP neural network and the actual tension values is 7.93%. After optimization using the particle swarm optimization algorithm, the mean absolute percentage error (MAPE) of the neural network prediction is reduced to 2.78%. Both of these values significantly outperform those obtained from the theoretical equations of string vibration. Moreover, the MAPE of PSO-BP also surpasses that of the optimized calculation formulas in the literature. Utilizing the PSO-BP neural network for tension prediction avoids inaccuracies in tension calculation caused by an excessive simplification of boundary conditions and a vague definition of effective cable length; thus, it possesses certain engineering practical value.