基于深度信念网络的盾构隧道施工安全研究

Research on Construction Safety of Shield Tunnel Based on DBN

针对广州地铁18号线盾构隧道工程施工的主要安全问题——地表沉降和管片上浮,基于现场监测得到的施工参数与安全问题间的海量大数据,采用深度学习网络——深度信念网络构建了施工安全预测模型,并研究了六个主要施工掘进参数对施工安全的影响。结果表明:深度信念网络可以根据现场监测大数据得到较准确的地表沉降和管片上浮预测值;且地表沉降量随土仓压力、注浆量和注浆压力的增大而减小,随千斤顶推力、刀盘扭矩和掘进速度的增大而增大。而管片上浮量随土仓压力和注浆压力的增大而增大,随千斤顶推力和注浆量的增大而减小,刀盘扭矩和掘进速度对管片上浮影响不大。

Aiming at the main safety problems of shield tunnel construction of Guangzhou Metro Line 18,which are presented by the surface subsidence and segment floating,the deep learning network-deep belief network is used to construct the prediction model for the construction safety based on the big data between construction parameters and safety problems obtained from field monitoring.The influence of six main parameters on construction safety is studied.The results show that the deep belief network can obtain more accurate prediction values of surface settlement and segment floating based on the big data of field monitoring.Moreover,the surface settlement decreases with the increase of the soil bin pressure,grouting volume and grouting pressure,and increases with the increase of the jack thrust,cutter torque and tunneling speed.The floating capacity of the segment increases with the increase of the soil bin pressure and grouting pressure,and decreases with the increase of the jack thrust and grouting volume.The torque of the cutter head and the tunneling speed have little effect on the floating of the segment.