基于改进GWO-BP神经网络模型的箱涵沉降预测

Prediction of Box Culvert Settlement Based on Improved GWO-BP Neural Network Model

箱涵受外部荷载等多方面影响,会出现一定程度的不均匀沉降,可能会对箱涵结构造成破坏,因此箱涵地基沉降预测十分重要。引入灰狼算法(GWO)对BP神经网络的权值和阈值进行寻优,建立了基于改进的GWO-BP预测模型,对箱涵的沉降值进行预测。将该预测模型应用于南水北调工程天津某标段的箱涵沉降预测,并将预测值与实测值进行对比,相对误差在5%以下。通过与未改进的灰狼算法优化BP神经网络模型、BP模型进行对比,结果表明改进的灰狼算法优化BP神经网络预测模型具有更好的寻优能力与寻优精度,能够有效地对箱涵沉降值进行预测。

Due to the influence of external load and other aspects,the box culvert will have a certain degree of uneven settlement,which may cause damage to the box culvert structure.Therefore,the prediction of box culvert foundation settlement is very important.The gray wolf algorithm(GWO)was introduced to optimize the weight and threshold of BP neural network,and the improved GWO-BP prediction model was established to predict the settlement value of box culvert.The prediction model was applied to the settlement prediction of a box culvert in Tianjin section of South-to-North Water Transfer Project,and the relative error was less than 5%.The results show that the Improved Grey Wolf algorithm has better optimization ability and accuracy,and can effectively predict the settlement value of box culvert.