摘要
隧道工程围岩的级别是隧道围岩稳定性的尺度,施工期间的隧道围岩分类的确定是最为基础、也是最为重要的内容。本文将粗糙集、小波神经网络和围岩分类有机结合起来,对白鹤隧道围岩分类进行识别研究。结果表明:用经过粗糙集约简后的样本集作为神经网络的训练样本集,有效地简化了神经网络的结构,减少了训练步数与训练时间,并提高了网络的学习速度和判断准确率;经过粗糙集约简后的WNN判别准确率最高,识别结果更接近专家质量评价法;而BP网络判别结果与专家质量评价法相差较大;总体上,小波神经网络预判的结果要比BP神经网络预判的结果精度要高,约简后要比约简前的精度要高。
The surrounding rock grade of a tunnel is the scale of tunnel stability.The determination of rock classification is the basic and most important content in the period of tunnel excavation.In this paper,the rough set,wavelet neural networks and rock classification were combined and applied to Baihe tunnel rock classification.The training sample set of neural network is reduced by rough set,the neural network structure is effectively simplified,the training steps and the training time are decreased,and the network learning speed and accuracy is improved.The judgement accuracy by using rough set WNN is the highest,and the recognition results are the nearest to the results by expert discriminant system.The results using BP neural network has the largest difference with the expert discriminant system value.In general,the prediction accuracy using the wavelet neural networks is higher than the prediction accuracy using BP neural network,and the prediction accuracy using rough set is higher than that without rough set.
出处
《地下空间与工程学报》
CSCD
北大核心
2011年第3期429-434,456,共6页
Chinese Journal of Underground Space and Engineering
基金
国家自然科学基金项目(40872170)
关键词
粗糙集
小波神经网络
隧道围岩分类
预测
稳定性
rough set
wavelet neural network
tunnel surrounding rock classification
prediction
stability
