摘要
利用BP神经网络较强的高次非线性映射能力和学习功能 ,建立了基于人工神经网络的高速公路路基沉降量的预测模型。该模型依据现场实测资料 ,避免了计算过程中各种人为因素的影响。通过对某高速公路高填方路基沉降量的现场监测成果的学习与预测检验 ,证明其预测精度与适用性良好 。
Through use of the stronger nonlinear mapping and learning ability of the back propagation neural network, the authors develop a new artificial neural network model to predict the settlement of highway foundation. This model avoids the errors caused by artificial factors during calculation since the model is established using all in-situ observation data. The results show that the model simulation matches well with in-situ observation of the highway foundation settlement, which demonstrates its applicability in the engineering practice.
出处
《工程地质学报》
CSCD
2004年第4期427-430,共4页
Journal of Engineering Geology
基金
上海市重点建设研究基金资助项目
湖南省自然科学基金资助项目 ( 0 0JJY0 5 5 )
