摘要
为准确掌握隧道大变形的发展规律,以支持向量机及M-K检验为基础,构建了隧道大变形的预测模型及趋势判断模型,且为保证预测精度,先利用试算法和粒子群算法优化支持向量机的模型参数,进一步利用混沌理论优化前者的残差序列,以逐步提高预测精度;同时,利用M-K检验判断隧道大变形的发展趋势,并与前述预测结果对比,实现隧道大变形规律的综合分析。实例检验表明:通过参数优化可有效提高预测精度,且混沌理论对残差序列的优化效果较好,保证了隧道大变形的高精度预测,且M-K检验的分析结果与预测结果一致,均得出分析断面的变形将会进一步增加,验证了2类模型在隧道大变形规律研究中的适用性,进而为隧道大变形规律研究提供一种新的思路。
In order to accurately understand the law of large deformation development of tunnels,based on support vector machine and M-K test,this paper established predictive model and tendency estimation model of tunnel large deformation,and firstly used trial-and-error method and particle swarm optimization to optimize the model parameters of the support vector machine for the guarantee of prediction accuracy,and then further applied the chaos theory to optimize the residual sequence of the former to gradually improve the prediction accuracy,while employed the M-K test to determine the development trend of tunnel large deformation.The development tendency was compared with the above-mentioned prediction results to obtain a comprehensive analysis of the law of large deformation of tunnels.The test shows that the prediction accuracy can be improved effectively by parameter optimization,and the chaos theory has better effect on the optimization of residual sequence and can ensure the high-precision prediction of the tunnel large deformation,and the analysis results of the M-K test are consistent with the prediction results that indicate the deformation of section will worsen,which proves the applicability of two types of models in the study on the law of large deformation of tunnels and provides a new way of thinking for such study.
作者
雷敏哲
鲁文娟
薛晓辉
Lei Minzhe;Lu Wenjuan;Xue Xiaohui(Shaanxi Railway Institute,Weinan 714000,China)
出处
《甘肃科学学报》
2020年第2期132-137,共6页
Journal of Gansu Sciences
关键词
隧道
大变形
支持向量机
变形预测
M-K检验
Tunnels
Large deformation
Support vector machine
Deformation prediction
M-K test