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首页> 外文期刊>Cold regions science and technology >Assessing frost heave susceptibility of gravelly soils based on multivariate adaptive regression splines model
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Assessing frost heave susceptibility of gravelly soils based on multivariate adaptive regression splines model

机译：基于多变量自适应回归均值模型评估砾石土的冰淇淋升降易感性

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摘要

Frost heave of railway roadbed leads to track geometry degradation during the cold season, seriously threatening the safety of high-speed trains. An accurate estimation of freezing-induced deformation in subgrade aggregates is thus critical to construction and maintenance of transportation infrastructure in seasonally frozen regions. This paper proposes a practical approach to assessing the frost heave susceptibility of gravelly soils under unidirectional freezing conditions. Typical frost heave tests are first performed on gravel columns in closed and open systems. The multivariate adaptive regression splines algorithm is subsequently applied to develop a predictive model for the normalized heave of a specimen. Experimental data of freezing tests were collected from this study and available literature to compile a dataset. A randomly selected subset is used for training, while the complement of the subset is intended for testing. Relative importance analysis and analysis of variance are finally performed to examine the general and coupling effects of initial moisture content, fines, relative compaction, and stress level on the frost heave susceptibility of compacted soil. Hopefully, the developed model and comprehensive analysis of coarse fills could assist railway agencies in understanding the appropriate characterization of frost heave and provide an evaluation guideline for optimized railway roadbed.
机译：铁路路基的霜冻导致寒冷季节的几何降解，严重威胁高速列车的安全。准确估计路基聚集体中的冷冻诱导的变形是关键于季节性冷冻区域的运输基础设施的构建和维护至关重要。本文提出了一种实用的方法来评估单向冷冻条件下砾石土壤的霜冻升温度。典型的霜冻升降测试首先在封闭和开放系统中的砾石柱上进行。随后应用多变量自适应回归样条算法以开发用于标本的标准化升降的预测模型。从该研究中收集了冷冻测试的实验数据，以及可用文献来编译数据集。随机选择的子集用于训练，而子集的补充旨在用于测试。最终进行相对重要性分析和方差分析，以检查初始水分含量，细粒，相对压实和应力水平对压实土壤的霜冻易感性的一般和耦合效应。希望，发达的模型和对粗填充的全面分析可以帮助铁路机构了解霜冻的适当表征，并为优化的铁路路基提供评估指南。

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  • 来源
    《Cold regions science and technology》 |2021年第1期|103182.1-103182.14|共14页
  • 作者

    Wang Tengfei; Ma Hongfei; Liu Jiankun; Luo Qiang; Wang Qingzhi; Zhan You;

  • 作者单位

    Southwest Jiaotong Univ Sch Civil Engn Chengdu 610031 Peoples R China|Southwest Jiaotong Univ MOE Key Lab High Speed Railway Engn Chengdu 610031 Peoples R China;

    Southwest Jiaotong Univ Sch Civil Engn Chengdu 610031 Peoples R China;

    Sun Yat Sen Univ Sch Civil Engn Guangzhou 510275 Peoples R China;

    Southwest Jiaotong Univ Sch Civil Engn Chengdu 610031 Peoples R China|Southwest Jiaotong Univ MOE Key Lab High Speed Railway Engn Chengdu 610031 Peoples R China;

    Qinghai Univ Sch Civil Engn Xining 810016 Peoples R China;

    Southwest Jiaotong Univ Sch Civil Engn Chengdu 610031 Peoples R China|Southwest Jiaotong Univ Highway Engn Key Lab Sichuan Prov Chengdu 610031 Peoples R China;

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  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

    Frost heave; Multivariate adaptive regression splines; Coarse-grained soil; High-speed rail;

    机译：霜冻;多变量自适应回归花键;粗粒土壤;高速轨道;

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