大跨度悬索桥锚碇基础基底土压力智能预测研究  

Intelligent Prediction of Anchorage Foundation Soil Pressure for Large Span Suspension Bridge

作　　者：任丽芳[1,2] 袁宝远[2] 

机构地区：[1]石家庄铁路职业技术学院经济管理系,石家庄050041 [2]河海大学地球科学与工程学院,南京201198

出　　处：《长江科学院院报》2011年第5期46-49,54,共5页Journal of Changjiang River Scientific Research Institute

基　　金：国家自然科学基金委员会、二滩水电开发有限责任公司雅砻江水电开发联合研究项目(50539110)

摘　　要：将灰色系统(GM(1,1))、BP神经网络、灰色神经网络(GNNM(1,1))3种智能预测模型分别应用于深大基坑锚碇基础的基底变形预测过程中,以润扬大桥北锚碇基础基底土压力的监测资料为例进行动态预测分析,并与实测值进行了比较。结果表明:3种模型土压力预测值的相对误差分别为1.11%,0.77%和0.43%。GNNM(1,1)模型的预测结果更接近于实测值,与GM(1,1)和BP神经网络相比,GNNM(1,1)更适宜对波动较大的线性数据和非线性数据进行拟合,可以在工程中推广应用。Intelligent models including Grey Model（GM（1,1））,BP neural network,and the combination of the two models Grey Neural Network Model（GNNM（1,1）） were employed in the prediction of anchorage foundation deformation.Monitored soil pressure of the north anchorage foundation of Runyang Bridge was taken to dynamically predict the deformation by these three models.The predictions were further compared with the measured soil pressures.The comparison showed that there is a relative error of 1.11%,0.77% and 0.43% respectively of each model＇s prediction result.Compared with the other two models,the prediction of GNNM（1,1） was closer to the measured soil pressure,and it can be applied in actual prediction process as it is more appropriate for curve fitting nonlinear data and large-fluctuation data.

关 键 词：锚碇基础 智能算法 变形预测 灰色神经网络 

分 类 号：U442.2[建筑科学—桥梁与隧道工程]