(中南大学 资源环境与建筑工程学院
采矿与岩土工程研究所,长沙 410083)
摘 要: 岩土流变是岩土工程失稳破坏的重要原因之一。从系统辨识的角度，首先将岩土材料流变本构的一般微分方程通式按实际的采样周期转化为线性时不变SISO系统的离散差分方程格式，构建了用于岩土流变本构模型辨识的BP神经网络模型；然后探讨了该神经网络模型用于岩土流变本构模型辨识的基本步骤以及其网络结构参数（输入层神经元数和网络连接权值）与SISO流变系统差分方程模型参数间相互转化的算法原理，并据此在Matlab软件平台中编制了BP网络辨识算法的相应程序CYJ1.M；最后，采用有关的考题验证证明该辨识算法是成功可信的。
关键字: 岩土材料; 流变; 本构模型; 系统辨识; 人工神经网络
（Institute of Mining and Rock Soil Mechanics Engineering， College of Resources, Environment and Civil Engineering , Central South University, Changsha 410083, China）
Abstract:The rheology of rock and soil is one of the important reasons why geotechnical engineering is often apt to lose its stability and be damaged. From the view of system identification, the author first changes the general constitutive differential equation of the rheology of rock and soil into the discrete difference equation of the linear time—invariant single—input single—output system using the actual sampling period and builds the BP neural network model to identify the rheological constitutive model of rock and soil, and then probes into the basic steps of the identification of the rheological constitutive model of rock and soil using the BP model and studies the algorithmic principle to translate the BP network structure parameters (the number of the network’s neurons and the values of the network’s connections weights) into the model parameters of the difference equation of the SISO rheological system. Based on the principle the author also compiles the application program CYJ1.M for the identification arithmetic using the BP neural network on the base of MATLAB software .At last, by using an examination question, the identification arithmetic is testified to be successful and reliable.
Key words: rock and soil material; rheology; constitutive model; system identification; artificial neural network
