索力振动法测量神经网络赋泛研究

Research on Neural Network Generalization of Cable Force Vibration Measurement

索的受力状态关系着索体系桥梁的安全,索力值是衡量索力学状态的重要指标。目前,索的边界条件难以判别是影响索力识别结果准确性的重要因素。为此,利用ANSYS对拉索振动进行数值模拟,并借助已有索力计算公式对建模方式的可靠性进行验证,并生成模拟数据;然后,以索长、线密度、抗弯刚度、1阶频率、2阶频率、3阶频率为输入参数,以索力值为输出参数,结合振动模拟数据,分别建立BP神经网络和广义回归神经网络索力预测模型,并将两种神经网络索力预测模型和已有索力计算公式应用于实际工程中,并进行对比验证。结果表明:BP神经网络索力预测模型的神经网络结构为6-13-13-1,输入层与隐含层1、隐含层1与隐含层2、隐含层2与输出层之间的激励函数分别为tansig、tansig、purelin,训练算法为L-M优化算法trainlm,学习速率为0.1,网络迭代次数为1000,显示间隔为100,均方误差为0.001,索力预测模型的预测效果良好,但还有进一步优化的空间;广义回归神经网络索力预测模型的最佳spread值为0.00215,索力预测模型的预测效果优于BP神经网络和已有索力计算公式,且预测误差基本控制在5%以内。利用广义回归神经网络对桥梁索力进行预测,避免了索的边界条件判别错误对索力识别结果准确性的影响,提高了索力的识别精度,具有良好的工程应用价值。

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 1000,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 0.00215,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.