摘要
为了准确预测铁路隧道突水风险等级,降低隧道施工过程中的突水灾害风险,结合相关规范,在调研分析影响隧道突水灾害的风险因素集的基础上遴选13个因素构建评价指标体系。利用主成分分析法对突水风险评价指标提取主成分并实现降维,模糊C-均值聚类算法计算RBF神经网络的中心,梯度下降法修正权值和方差,并将分析后得到的主成分作为改进RBF神经网络评价模型输入向量,建立了基于PCA-改进RBF神经网络铁路隧道突水风险评价模型。最后结合天秀山隧道对该模型预测效果进行验证,评价结果与实际情况相符。实例研究表明:该模型合理可操作,相比于其他方法准确率更高、训练更快、均方误差更小,为类似铁路隧道预防突水灾害事故提供了一种新的途径和借鉴。
In order to accurately predict the risk level of water inrush in railway tunnel and reduce the risk of water inrush accident in the process of tunnel construction,combined with the relevant specifications,13 factors were selected to build the evaluation index system based on the investigation and analysis of the risk factor set of water inrush disaster in tunnel.The principal component analysis was used to extract and reduce the dimension of water inrush risk evaluation index.Fuzzy c-means clustering algorithm was used to calculate the center of RBF neural network.Gradient descent method was used to modify the weight and variance.The principal component obtained after analysis was used as the input vector of the improved RBF neural network evaluation model.The PCA-improved RBF neural network based railway tunnel water inrush risk evaluation model was established.Finally,the prediction effect of the model is verified by combining with Tianxiushan tunnel.And the evaluation result was consistent with the actual situation.The case study shows that the model is reasonable and operable.Compared with other methods,it has higher accuracy,faster training and smaller mean square error.It provides anew way and reference for similar railway tunnels to prevent water inrush accidents.
作者
魏晓悦
靳春玲
贡力
张鑫
马梦含
WEI Xiaoyue;JIN Chunling;GONG Li;ZHANG Xin;MA Menghan(School of Civil Engineering,Lanzhou Jiaotong University,Lanzhou 730070,China;Research on Water Transfer Project and Water Transport Safety of Lanzhou Jiaotong University,Lanzhou 730070,China)
出处
《铁道科学与工程学报》
CAS
CSCD
北大核心
2021年第3期794-802,共9页
Journal of Railway Science and Engineering
基金
国家自然科学基金资助项目(51969011,51669010)。
