检索结果-南通市图书馆

adaptive mutation sparrow search algorithm-Elman-AdaBoost model for predicting the deformation of subway tunnels

Underground Space 2024年第4期17卷 320-360页

作者： Xiangzhen Zhou Wei Hu Zhongyong Zhang Junneng Ye Chuang Zhao Xuecheng Bian MOE Key Laboratory of Soft Soils and Geoenvironmental Engineering College of Civil Engineering and ArchitectureZhejiang UniversityHangzhou 310058China Hangzhou Canal Group Construction Management Co.LTD Hangzhou 310015China Ningbo Rail Transit Project Construction Headquarter Ningbo 315012China Center for Hypergravity Experimental and Interdisciplinary Research Zhejiang UniversityHangzhou 310058China

A novel coupled model integrating Elman-AdaBoost with adaptive mutation sparrow search algorithm(AM-SSA),called AMSSAElman-AdaBoost,is proposed for predicting the existing metro tunnel deformation induced by adjacent deep excavations in soft *** novelty is that the modified SSA proposes adaptive adjustment strategy to create a balance between the capacity of exploitation and *** AM-SSA,firstly,the population is initialized by cat mapping chaotic sequences to improve the ergodicity and randomness of the individual sparrow,enhancing the global search *** the individuals are adjusted by Tent chaotic disturbance and Cauchy mutation to avoid the population being too concentrated or scattered,expanding the local search ***,the adaptive producer-scrounger number adjustment formula is introduced to balance the ability to seek the global and local *** addition,it leads to the improved algorithm achieving a better accuracy level and convergence speed compared with the original *** demonstrate the effectiveness and reliability of AM-SSA,23 classical benchmark functions and 25 IEEE Congress on Evolutionary Computation benchmark test functions(CEC2005),are employed as the numerical examples and investigated in comparison with some wellknown optimization *** statistical results indicate the promising performance of AM-SSA in a variety of optimization with constrained and unknown search *** utilizing the AdaBoost algorithm,multiple sets of weak AMSSA-Elman predictor functions are restructured into one strong predictor by successive iterations for the tunnel deformation prediction ***,the on-site monitoring data acquired from a deep excavation project in Ningbo,China,were selected as the training and testing ***,the predictive outcomes are compared with those of other different optimization and machine learning *** the end,the obtained results in this real-world geotechnical eng

关键词： Adjacent deep excavations Existing subway tunnels adaptive mutation sparrow search algorithm Metaheuristic optimization Benchmark test functions Elman neural networks

来源：

维普期刊数据库评论

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

欢迎您,

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

在线全文

请选择保存的检索档案：

请选择收藏分类：

通借通还

欢迎您,

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

在线全文

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：