基于ABC-BP神经网络的地铁盾构地表沉降预测

Prediction of ground settlement of subway shield based on ABC-BP neural network

为研究地层参数和盾构掘进参数与地表沉降的非线性关联性,依托南京地铁6号线盾构区间,采用人工蜂群算法ABC优化BP神经网络,建立可预测地表沉降的ABC-BP神经网络模型。连续3个断面地表沉降预测结果表明:ABC-BP神经网络的预测精度和预测稳定性优于BP神经网络,且预测值与实测值一致;ABC-BP神经网络可较为准确地反映盾构机接近监测断面过程中的地表变形演变规律,最终实现地表变形控制的目的。提出了ABC-BP神经网络现场应用思路,构建了地层-掘进参数-沉降的关系,进而通过地层参数直接实现对盾构掘进参数和地表变形控制。

To study the nonlinear correlation between strata parameters,shield tunneling parameters and ground settlement,an ABC-BP neural network model that can predict the ground settlement was established by using the artificial bee colony(ABC)algorithm and BP neural network based on the shield interval of Nanjing Metro Line 6.This model was validated through three consecutive sections of the shield.The results show that the prediction accuracy and prediction stability of ABC-BP model are better than BP model,and the predicted value is consistent with the real value.The model can accurately reflect the evolution law of ground deformation during the shield machine approaching the monitoring section and achieve the purpose of final ground deformation control.This study proposes the on-site application concept of ABC-BP neural network,constructs the relationship between strata parameters,excavation parameters and settlement,and can directly control shield tunneling parameters and ground deformation through strata parameters.