摘要
以现场实测资料为依据,建立了抗滑结构位移预测的神经网络模型,分别用于抗滑桩顶位移预测和抗滑桩沉降预测,并与灰色模型的预测结果进行了比较,结果表明,神经网络的预测结果更接近于实际值,从而验证了神经网络用于抗滑结构位移预测的可行性。神经网络收敛快、拟合效果好、泛化能力强、预测精度高,是抗滑结构位移预测的有效方法。
Based on the in-situ survey data, the neural network model for displacement prediction of anti-slide structures was established and separately used for predicting displacement at the top of anti-slide pile and settlement of anti-slide pile. Results obtained were compared with that from grey model. It shows that the prediction results by the neural network model are closer to the actual value, proving that it is feasible for the neural network model to be applied in the displacement prediction of anti-slide structures. The neural network model, with better convergence and fitting effect, stronger generalization ability, higher accuracy in prediction, is an efficient method for predicting displacement of antislide structures.
出处
《矿冶工程》
CAS
CSCD
北大核心
2009年第5期13-15,共3页
Mining and Metallurgical Engineering
基金
湖南省重点科技攻关项目(05sk2008)
湖南省自然科学基金(05JJY2056)
关键词
岩土工程
神经网络
抗滑结构
位移
沉降
预测
geotechnical engineering
neural network
anti-slide structure
displacement
settlement
prediction
