人工神经网络在澜沧江某电站坝基右岸复杂岩体分类中的应用

The application of artificial neural network in complicated rockmass quality classification of a hydraulic power station on the right bank of Lancang River

结合云南省澜沧江某水电站工程实例,应用BP人工神经网络方法建立3层BP网络模型。选取岩石单轴抗压强度、岩体完整性系数、RQD、节理面粗糙度系数、节理面风化变并系数、透水性系数等6个影响因素为输入变量,对坝基右岸复杂岩体进行质量分类。通过对比分析发现,BP网络模型经多次学习后,预测岩体质量类别时辩识能力较强,结果可靠,取得了较好的实际应用效果。

The paper applies the method of the back propagation artificial neural network on a hydraulic power station on the Lancang River, Yunnan Province,choosing six influential factors on the rock mass quality as the input variables,such as one axis compressive strength of rock, sound degree coefficient of rock mass, rock quality designation, roughness degree coefficient of joint surface, alteration coefficient of joint surface, permeability coefficient,and classifies the complicated rock mass on the right bank of the dam foundation. Through comparison analysis, the result of predicting the rock mass is exact and credible after the model of the back propagation artificial neural network learning enough times.The application has gained a good effect.