摘要
针对分级加载下用线性叠加原理来描述岩土非线性流变存在的问题,采用人工神经网络方法代替传统数学力学方法,建立了分级加载下岩土流变的神经网络模型,并且对具体岩石的蠕变试验曲线进行了模拟。结果表明,该模型能较好地描述岩土的非线性流变,具有较强的泛化能力,为研究岩土的流变特性特别是分级加载下岩土的非线性流变特性提供了一条新途径。
In light of the problem of describing nonlinear rheology of rock and soil with principle of linear superposition, a neural network model for rheology of rock and soil under step loading has been established with artificial neural network instead of traditional mathematical and mechanical methods. By simulating the experimental creep curves of gypsum breccias, it is shown that the model can effectively describe nonlinear rheology of rock and soil with better prediction. This approach provides a new way for studying rheologicat properties of rock and soil especially nonlinear rheological properties under step loading.
出处
《岩土力学》
EI
CAS
CSCD
北大核心
2006年第7期1187-1190,共4页
Rock and Soil Mechanics
关键词
流变
分级加载
非线性
神经网络
模型
rheology
step loading
nonlinearity
neural network
model
