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基于贝叶斯-粒子群算法的溜砂坡稳定性评价

娄超华 田荣燕 旺久 孙威宇 罗进

娄超华, 田荣燕, 旺久, 孙威宇, 罗进. 2021: 基于贝叶斯-粒子群算法的溜砂坡稳定性评价. 中国地质灾害与防治学报, 32(2): 53-59. doi: 10.16031/j.cnki.issn.1003-8035.2021.02.07
引用本文: 娄超华, 田荣燕, 旺久, 孙威宇, 罗进. 2021: 基于贝叶斯-粒子群算法的溜砂坡稳定性评价. 中国地质灾害与防治学报, 32(2): 53-59. doi: 10.16031/j.cnki.issn.1003-8035.2021.02.07
Chaohua LOU, Rongyan TIAN, Jiu WANG, Weiyu SUN, Jin LUO. 2021: Stability evaluation of sand slopes based on the Bayesian-PSO algorithm. The Chinese Journal of Geological Hazard and Control, 32(2): 53-59. doi: 10.16031/j.cnki.issn.1003-8035.2021.02.07
Citation: Chaohua LOU, Rongyan TIAN, Jiu WANG, Weiyu SUN, Jin LUO. 2021: Stability evaluation of sand slopes based on the Bayesian-PSO algorithm. The Chinese Journal of Geological Hazard and Control, 32(2): 53-59. doi: 10.16031/j.cnki.issn.1003-8035.2021.02.07

基于贝叶斯-粒子群算法的溜砂坡稳定性评价

doi: 10.16031/j.cnki.issn.1003-8035.2021.02.07
基金项目: 西藏自治区重点科技计划项目(XZ201901-GB-14);西藏大学大学生创新创业训练计划项目(2018QCX016)
详细信息
    作者简介:

    娄超华(2000-),男,河南南阳人,本科,主要研究方向为道路沿线灾害。E-mail:lch420@qq.com

    通讯作者:

    田荣燕(1980-),女,重庆长寿人,硕士,副教授,主要研究方向为道路灾害。E-mail:21675279@qq.com

  • 中图分类号: P642.22

Stability evaluation of sand slopes based on the Bayesian-PSO algorithm

  • 摘要: 溜砂坡具有突发、不易预测,且产生危害大的特点。文章对拉萨市周边实地调研测量收集数据,采集了12组具有代表性的溜砂坡灾害点数据集合,运用贝叶斯网络与粒子群算法相结合,并利用算法更新公式弥补单一算法的不足,引入信息熵分析了降雨量、坡度、坡高和植被覆盖率在算法中的权重,以及各因素对溜砂坡稳定性的影响,并对溜砂坡的稳定性进行了等级划分,实验证明该方法有效,对溜砂坡稳定性评价具有一定参考价值。
  • 图  1  $ M $运算逻辑图

    Figure  1.  Logic diagram of M algorithm

    图  2  $ C $运算逻辑图

    Figure  2.  Logic diagram of C algorithm

    图  3  BN−PSO流程图

    Figure  3.  BN−PSO flowchart

    表  1  灾害点原始数据

    Table  1.   Raw date of disaster points

    样本a平均降雨量/mm坡高/m坡度/(°)植被覆盖率/%
    灾害点1488.314.022.012.0
    灾害点2494.458.436.017.0
    灾害点3493.758.060.014.0
    灾害点4487.219.042.011.0
    灾害点5495.07.027.013.0
    灾害点6490.492.037.217.0
    灾害点7492.449.050.012.0
    灾害点8485.673.045.016.0
    灾害点9477.422.537.019.0
    灾害点10493.225.540.512.0
    灾害点11482.319.528.011.0
    灾害点12486.292.042.017.0
    下载: 导出CSV

    表  2  溜砂坡评价指标等级划分及标准

    Table  2.   Classification and standardization of evaluation factors of debris slope stability

    评价指标指标分类划分标准(分值)X
    0.1~0.20.3~0.40.5~0.60.7~0.80.9~1.0
    平均降雨量/mm≤300300~400400~500500~600≥600
    坡高/m≤1010~2020~3030~40≥40
    坡度/(°)<2020~3030~4040~50≥50
    植被覆盖率/%≥7070~5050~3030~10≤10
    下载: 导出CSV

    表  3  碎屑斜坡稳定性影响因素的信息熵及权重

    Table  3.   Entropy and weight of evaluation factors of debris slope stability

    评价指标信息熵(EGD)权重(w)权重排序
    植被覆盖率0.75260.22624
    坡度0.64240.26472
    降雨量0.63520.28011
    坡高0.70920.22903
    下载: 导出CSV

    表  4  灾害点数据处理结果

    Table  4.   Date processing results of disaster points

    BIC值BIC排序稳定等级稳定状态
    灾害点10.353 47较稳定
    灾害点20.408 95稳定
    灾害点30.338 69较不稳定
    灾害点40.490 43不稳定
    灾害点50.340 08基本稳定
    灾害点60.682 31不稳定
    灾害点70.327 710不稳定
    灾害点80.307 712不稳定
    灾害点90.375 26稳定
    灾害点100.462 14较不稳定
    灾害点110.316 611不稳定
    灾害点120.675 42不稳定
    下载: 导出CSV
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  • 收稿日期:  2020-05-03
  • 修回日期:  2020-06-20
  • 刊出日期:  2021-04-27

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