安全与环境工程2024，Vol.31Issue(6)：48-56,9.DOI:10.13578/j.cnki.issn.1671-1556.20231272
基于NMI-SS-FOA优化极限学习机的隧道变形预测模型
Prediction model of tunnel deformation based on NMI-SS-FOA optimized extreme learning machine
摘要
Abstract
To ensure that tunnel construction meets the requirements of safety,cost-effectiveness,and efficiency,accurate prediction of tunnel deformation is necessary.Considering the nonlinearity and time-series characteristics of tunnel deformation data,this paper proposes a tunnel deformation prediction model based on NMI-SS-FOA optimized extreme learning machine(ELM).The model first used the normalized mutual information(NMI)method to screen key parameters affecting tunnel deformation,and then used the SS-FOA-ELM which is optimized by the fruit fly algorithm with sector search mechanism to predict tunnel deformation,comparing the prediction results with those of statistical prediction methods random forest method,BP neural network model,ELM model,and support vector regression(SVR)model.The research results show that the tunnel deformation prediction model based on NMI-SS-FOA optimized ELM can effectively predict tunnel deformation.The corresponding root mean square error(ERMSE),mean absolute percentage error(ERMSE),a10 index(a10),and coefficient of determination(R2)are 5.06,19.42%,0.932,and 0.607,respectively,indicating better prediction performance than other models.The thickness of the covering layer(H),the cohesion of the rock mass(Crm),and the internal friction angle of the rock mass(ϕrm)have a significant impact on the prediction results.The research results can provide reference for the prediction and control of tunnel deformation caused by tunnel construction.关键词
隧道工程/变形预测/优化算法/极限学习机/归一化互信息法Key words
tunnel engineering/deformation prediction/optimization algorithm/extremelearning machine/normalized mutual information method分类
资源环境引用本文复制引用
姜平,徐剑波,杨熙,许文军,任若微,罗学东..基于NMI-SS-FOA优化极限学习机的隧道变形预测模型[J].安全与环境工程,2024,31(6):48-56,9.基金项目
国家自然科学基金项目(42072309) （42072309）
