融合蚁群算法和BP神经网络的基坑变形预测应用研究

Application Research on Deformation Prediction of Foundation Pit Integrating Ant Colony Algorithm and BP Neural Network

为了保证基坑工程和周边建筑物的安全,需要对基坑变形进行预测。BP神经网络因其权阈值易陷入局部最优的问题导致无法准确进行基坑变形预测。融合蚁群算法的全局最优特点,提取BP神经网络全局最优权阈值。通过定量精度分析结果,验证融合预测模型的准确性,为基坑变形预测提供了有效的技术手段。

In order to ensure the safety of foundation pit engineering and surrounding buildings,it was necessary to predict the deformation of foundation pit.In the prediction process,it was easy for BP neural network to generate the problem of local optimal weight threshold,which made it unable to accurately predict the deformation of foundation pit.In this paper,the global optimal characteristics of the ant colony algorithm were combined to extract the global optimal weight threshold of the BP neural network.Through quantitative results,the fusion prediction model was verified.The accuracy of the prediction model provided technical methods for the prediction of foundation pit deformation.