摘要
吸力内摩擦角在非饱和土抗剪强度理论中是一个关键参数,因受设备、测试技术以及试验方法等的限制,对于其值的确定难度较大。详细地分析了土的基本物理力学性质、测试方法对吸力内摩擦角数值的影响,并通过定性分析和定量计算结合,认为干密度、含水率、内摩擦角、黏聚力与吸力内摩擦角之间存在着一定的非线性关系,因而借用人工智能——BP神经网络的方法构建了4:9:1的网络结构模型,实现了对吸力内摩擦角φb的预测和计算。通过预测结果的分析可知,该模型模拟和预测的精度均较高,因此,该种方法可以应用到非饱和土吸力内摩擦角值的预测之中去。
In shearing strength theory for unsaturated soil,the suction friction angle is a key parameter,it is difficult to determine its value,which is limited by the test of equipment,technology and method.The influence factors for φ b such as the soils' basic physico-mechanical properties and the test method are analyzed detailedly.At the same time,by using qualitative analysis and quantitative calculation methods,it is considered that those factors have a certain degree of nonlinear relationship,which includes dry density,water content and internal friction angle.Based on artifical intelligence——the BP neural network,the 4:9:1 network structure is built to calculate and predict the suction internal friction angle for unsaturated soils.The validity of the model shows that the precisions of simulation and prediction are high.This model can be applied to forecast the index of the suction internal friction angle φ b.
出处
《岩土力学》
EI
CAS
CSCD
北大核心
2009年第S2期22-27,共6页
Rock and Soil Mechanics
基金
国家自然科学基金重点项目(No.90510017)
国家自然科学基金资助项目(No.50679073)资助
