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基于CatBoost-SHAP模型的滑坡易发性建模及可解释性

曾韬睿, 王林峰, 张俞, 程平, 吴帆

曾韬睿,王林峰,张俞,等. 基于CatBoost-SHAP模型的滑坡易发性建模及可解释性[J]. 中国地质灾害与防治学报,2024,35(1): 37-50. DOI: 10.16031/j.cnki.issn.1003-8035.202309035
引用本文: 曾韬睿,王林峰,张俞,等. 基于CatBoost-SHAP模型的滑坡易发性建模及可解释性[J]. 中国地质灾害与防治学报,2024,35(1): 37-50. DOI: 10.16031/j.cnki.issn.1003-8035.202309035
ZENG Taorui,WANG Linfeng,ZHANG Yu,et al. Landslide susceptibility modeling and interpretability based on CatBoost-SHAP model[J]. The Chinese Journal of Geological Hazard and Control,2024,35(1): 37-50. DOI: 10.16031/j.cnki.issn.1003-8035.202309035
Citation: ZENG Taorui,WANG Linfeng,ZHANG Yu,et al. Landslide susceptibility modeling and interpretability based on CatBoost-SHAP model[J]. The Chinese Journal of Geological Hazard and Control,2024,35(1): 37-50. DOI: 10.16031/j.cnki.issn.1003-8035.202309035

基于CatBoost-SHAP模型的滑坡易发性建模及可解释性

基金项目: 国家自然科学基金联合基金项目(U22A20600);重庆市研究生导师团队建设项目(JDDSTD2022009);重庆人才计划创新与创业示范团队(CQYc-201903204);国家自然科学基金项目(51708068)
详细信息
    作者简介:

    曾韬睿(1995—),男,重庆,博士研究生,主要从事滑坡灾害风险评价与管理研究。E-mail:zengtaorui@cug.edu.cn

    通讯作者:

    王林峰(1983—),男,教授,博士,研究方向为地质灾害减灾理论与技术。E-mail:wanglinfeng@cqjtu.edu.cn

  • 中图分类号: P642.22

Landslide susceptibility modeling and interpretability based on CatBoost-SHAP model

  • 摘要:

    文章致力于深入探索滑坡易发性建模中集成学习模型的不确定性和可解释性。以浙江省东部沿海山区为研究对象,利用谷歌历史影像与Sentinel-2A影像,记录了2016年超级台风“鲇鱼”触发的552起浅层滑坡事件。研究首先对连续型因子进行了不分级、等间距法和自然断点法的工况设计,进一步划分为4,6,8,12,16,20级。随后,引入了类别增强提升树模型(CatBoost)以评估不同工况下的滑坡易发性值,再结合受试者曲线与沙普利加性解释法分析,对建模过程中的不确定性和可解释性进行了深入研究,目的在于确定最优建模策略。结果表明:(1) 在CatBoost模型计算中,河流距离成为最关键的影响因子,其次是与地质条件、人类活动相关的因子;(2) 不分级工况下,模型能够获得最高的AUC值,达到0.866;(3)相较于等间距法,自然断点法的划分策略展现出更佳的泛化能力,且模型预测性能随着分级数量的增加而增加;(4)沙普利加性解释法模型揭示了主要影响因子道路距离、河流距离、DEM和坡向对台风诱发滑坡的控制机制。研究成果能够加深对滑坡易发性的理解,提高滑坡预测的准确性和可靠性,为相关地区的防灾减灾工作提供科学依据。

    Abstract:

    This study is dedicated to delving deeply into the uncertainty and interpretability of ensemble learning models in landslide susceptibility modeling. Focusing on the eastern coastal mountainous region of Zhejiang Province as the study area, this research utilizes historical Google imagery and Sentinel-2A imagery to document 552 shallow landslide events triggered by the super typhoon "Megi" in 2016. Initially, the study designs scenarios for continuous factors using non-grading, equal interval method, and natural breaks method, subsequently subdividing them into 4, 6, 8, 12, 16, 20 levels. Thereafter, the Category Boosting Model (CatBoost) is introduced to assess landslide susceptibility values under different scenarios. Coupled with the analysis of ROC (receiver operating characteristic) curves and SHAP (SHapley Additive exPlanation), in-depth investigation into uncertainty and interpretability during the modeling process is conducted, with the aim of determining the optimal modeling strategy. The results indicate that: (1) In the computations of the CatBoost model, aspect emerges as the most critical influencing factor, followed by factors related to water and geological conditions; (2) Under the non-grading scenario, the model achieves the highest AUC value, reaching 0.866; (3) Compared to the equal interval method, the natural breaks method demonstrates superior generalization capability, and the model’s predictive performance imrpoves with an increase in the number of classifications; (4) The SHAP model reveals the controlling mechanisms of the principal influencing factors (aspect, lithology, elevation, and road distance) on typhoon-induced landslides. The findings of this research can deepen our understanding of landslide susceptibility, enhance the accuracy and reliability of landslide predictions, and provide a scientific basis for disaster prevention and mitigation efforts in the related regions.

  • 近年来,随着国家对天然气管道工程建设的重视力度不断提高,我国的天然气管道工程建设步伐不断加快,目前国家干线管道系统已初具规模,并加速形成“主干互联、区域成网”的全国天然气基础网络[1]

    由于管道线路较长,通常跨越不同地区,沿线地质地貌条件复杂,因此在建设过程中难以避免会穿越不良地质地段,在不稳定因素的影响下极有可能诱发地质灾害,其中滑坡是山区油气管道工程中常见的地质灾害类型[2]。在滑坡作用下,管道容易产生变形甚至破损,导致燃气泄漏,严重威胁沿线民众的正常生活。因此,如何对管道滑坡进行有效的支护并保护管道安全值得关注。

    花管微型桩是一种新型压力灌浆式抗滑桩,集中了钢花管桩与微型桩的优点,在20世纪80年代开始在国内外应用并很快在基坑支护、滑坡治理等领域被广泛采用。目前,国内外学者对花管微型桩加固滑坡的承载性能已经开展了部分研究工作,但针对管道—滑坡体系支护性能研究方面还未涉及。潘锋[3]提出了一种注浆钢花管桩加固滑坡的理论计算公式;陈强等[4]通过离心模型试验研究发现注浆钢管桩在控制滑坡土体位移方面的有益作用;Wang等[5]针对钢花管桩微型桩的双注浆技术展开了研究,并通过试验证明了此工艺可以有效提高微型桩承载性能。

    螺纹微型桩来源于地基基础工程中使用的螺纹桩,因其利用“螺丝钉比钉子更牢固”的原理[6],使螺纹桩拥有较好的承载性能,但螺纹桩目前只在地基工程中应用较多,还未被作为支挡结构应用在滑坡治理工程中。国内外对其进行的研究多集中在竖向承载性能方面,如Krasiński[7]采用数值模拟方法对螺纹桩承载机理进行了研究,并通过现场载荷试验验证了方法的可靠性;方崇等[8]通过静载试验分析了螺杆桩的竖向承载力传递特性与受力特征;叶阳升等[9]对高速铁路中的螺杆桩复合地基进行了原位测试试验,研究获得了直杆端与螺纹段在不同受力状态下的桩侧摩阻力关系;Malik等[10]进行了螺纹桩与直杆桩承载性能的对比研究,结果证明螺纹桩轴径比为2~4.1的螺旋桩端承载力比类似桩轴径的直杆桩高2~12倍;孟振等[11]通过模型试验手段针对砂土地基中的螺杆桩承载特性展开了研究。

    为探究花管微型桩与螺纹微型桩加固管道—滑坡体系下桩体变形特征、桩体两侧土压力的空间分布规律以及在两种不同微型桩加固保护下管道的变形特点及破坏模式,进一步分析对比两种不同微型桩的支护性能,本文以中贵天然气管道K558+700滑坡工程为依托,以千斤顶模拟滑坡水平推力[12],开展7种不同水平加载压力工况下的室内大型推桩模型试验,并对桩顶位移、桩体应变、桩体两侧土压力、天然气管道应变以及管道两侧的土压力进行测试。进一步分析桩体弯矩以及桩体两侧峰值土压力空间分布规律,确定不同微型桩桩体变形特征,并结合管道变形破坏情况,探究两种微型桩在管道滑坡下的承载特性,总结试验成果,为我国管道地段滑坡灾害治理提供理论参考。

    中贵天然气管道K558+700滑坡治理工程位于甘肃省陇南市成县黄陈镇中湾村。2020年8月12—18日中湾村附近连降暴雨,导致中贵天然气管道K558+700处斜坡发生明显滑动变形,管道滑坡区域现场见图1。共产生2处滑坡及1处滑塌体。其中,中贵天然气管道位于H1滑坡中部,横坡敷设通过,管径1016 mm,管道走向186°,滑坡区管道埋深2.2~4.1 m。H1滑坡纵长约190 m,宽约186 m,滑坡平面面积约2.25×104 m2,滑体平均厚度约8.5 m,滑体总体积约19.13×104 m2,属于中型土质滑坡。该滑坡地表变形强烈,尤其是与管道斜交的乡村水泥道路已完全损毁,受滑坡推挤作用,威胁管道安全运营。

    图  1  管道滑坡区域现场地貌图
    Figure  1.  Site geomorphology map of pipeline landslide area

    本次试验设计以中贵天然气管道K558+700处H1滑坡典型断面为工程原型,参考试验模型箱尺寸,确定试验模型的几何相似比尺为:

    CL=LPLM=30 (1)

    式中:CL——几何相似常数;

    LP——原型尺寸;

    LM——模型尺寸。

    根据模型试验中各因素对现象影响的大小,抓住其起主要作用的因素,略去其次要因素的原则。以微型桩几何尺寸(L)、密度(ρ)、重力加速度(g)为主要控制参数,其相似比分别为30∶1、1∶1、1∶1,其余参数相似比可根据Buckinghamπ定理导出,如表1所示。

    表  1  相似比设计
    Table  1.  Design of similarity constant
    物理量相似比物理量相似比
    几何尺寸CL=30变形模量CE=30
    质量密度Cρ=1摩擦角Cφ=1
    重度Cy=1黏聚力CC=30
    应变Cϵ=1时间Ct=30
    位移Cu=30重力加速度Cg=1
    下载: 导出CSV 
    | 显示表格

    制作与原型形状完全相同的螺纹桩、花管桩较为困难,因此对其桩体模型进行简化,采用阻氧双色PP-R管材通过注水泥浆模拟桩体,其中花管桩模型为在PP-R管材长度方向上每隔8 cm环绕管道螺旋打孔并采用压力灌注水泥浆模拟;螺纹桩模型为在PP-R管材外径螺旋缠绕塑料软管并灌注水泥浆进行模拟;承台采用硬质木板模拟;天然气管道采用直径20 cm的PVC管模拟。制作完成的模型桩见图2

    图  2  桩体模型实物图
    Figure  2.  Physical drawing of pile model

    滑坡岩土体相似材料较为复杂,在配比过程中不易满足推导得到的相似关系,因此试验选取滑体、滑带及基岩材料时,以最易影响原型滑坡岩土体稳定性的若干参数 (容重、黏聚力、内摩擦角、弹性模量等)为基础,参考其他学者已得到的部分研究成果[13],通过正交配比设计,并经直剪试验和三轴试验检验参数取值的准确合理性,确定使用红粉土、石英砂、水泥、石膏、水的混合物模拟Ⅳ级基岩,具体质量配比为70∶30∶5∶3∶10;滑体采用红粉土、石英砂、水的混合物模拟,具体质量配比为70∶20∶10;滑动带采用石英砂、土、滑石粉、水的混合物模拟,具体质量配比为27∶52∶35∶15。得到的模型材料相关力学参数及其与原型材料的对比见表2

    表  2  模型材料与原型材料相关物理性质参数
    Table  2.  Physical property parameters related to model material and prototype material
    物理力学参数重度/(kN·m−3内摩擦角/(°)黏聚力/kPa弹性模量/MPa
    滑体原型19.030.040.0/
    模型19.030.51.2/
    滑带原型18.525.030.0/
    模型18.025.01.0/
    基岩原型27.0//5000
    模型26.7//160
    下载: 导出CSV 
    | 显示表格

    根据相似设计,试验使用的模型箱尺寸为 4 m×2 m×2 m(长×宽×高),箱体两侧由钢板及有机玻璃组成,箱体内填筑土体,从上往下分三层:滑体、滑带及基岩层。箱体内共布置4组微型桩群模型,其中左侧2组为花管桩,右侧2组为螺纹桩;桩后滑体内埋置有天然气管道模型。箱体后侧设置有反力墙,反力墙上安装有液压千斤顶,千斤顶通过作用在承压板上从而给滑坡模型施加水平推力。模型整体示意图如图3

    图  3  试验模型设计整体示意图
    Figure  3.  Schematic diagram of test model design

    微型桩群桩模型单桩布置形式为5行×3列,共布置4组微型群桩,以模型箱中线为分界线分为左右两侧,左侧2组为花管桩,右侧2组为螺纹桩;单桩桩体模型长160 cm,直径2.5 cm。微型桩群桩埋深160 cm,从桩顶到桩底依次穿过滑体、滑带与基岩层,其中滑体层厚75 cm,滑体坡面角40°,滑带厚度5 cm,基岩层厚100 cm;天然气管道布置在桩前滑体中,距离桩体20 cm,埋深40 cm。桩顶采用承台方式将群桩连接为整体,单桩中心间距为7 cm,承台长38 cm,宽24 cm,厚2 cm,如图4所示。

    图  4  承台及单桩模型布置设计图(单位:cm)
    Figure  4.  Cap and single pile model layout design drawing (unit: cm)

    试验数据采用DH3816N静态应变测试系统采集,该系统具有60个采集通道,可同时采集应变、土压力等测试数据。

    选取模型箱体中间两组微型桩群桩作为研究对象,在桩顶承台设置百分表测试桩顶位移,并在群桩中选取不同位置的典型单桩作为应变测试桩粘贴应变片,百分表设置位置及测试单桩的位置如图5所示。

    图  5  应变测试桩分布
    Figure  5.  Strain test pile distribution

    测试单桩为2#花管桩及3#螺纹桩群桩中的第二列单桩,并对其进行编号,从桩后到桩前依次为1、2、3号测试单桩,每一测试单桩在桩体两侧沿桩体深度间隔一定距离粘贴应变片,每根测试桩体上共粘贴2×6=12个应变片,所有测试桩体共布设12×6=72个应变片,具体布设位置如图6所示(以1号测试单桩为例,其余测试桩相同)。

    图  6  桩体应变片布设位置(单位:cm)
    Figure  6.  Position of pile strain gauge (unit: cm)

    天然气管道山侧与河侧对称粘贴3组应变片,一组位于花管桩侧中部,一组位于螺纹桩侧中部,以监测在两种不同微型桩群桩支护作用下天然气管道的变形并作为对比,进一步优化天然气管道滑坡的支护方案设计;另一组粘贴在天然气管道接口处,以监测管道接口处的变形,进一步研究在滑坡作用下天然气管道接口处的处理方法。天然气管道应变片布设位置见图7

    图  7  天然气管道应变片布设位置(单位:cm)
    Figure  7.  Location of strain gauge for natural gas pipeline (unit: cm)

    为了研究花管微型桩与螺纹微型桩支护下桩周土压力空间分布规律,在2#群桩与3#群桩前后沿桩体深度间隔一定距离对称布置土压力盒,布设位置如图8,共布设6×2×2=24个土压力盒,以监测在滑坡水平推力作用下两种微型桩群桩前后不同深度位置土压力的变化,以进一步分析两种不同桩体变形特征。

    图  8  群桩前后土压力盒布设位置(单位:cm)
    Figure  8.  Position of earth pressure cell before and after pile group (unit: cm)

    在天然气管道前后两侧对称布设土压力盒,布设位置分别位于花管桩支护侧、螺纹桩支护侧及管道接口附近,共布设3×2=6个土压力盒,以监测天然气管道附近土体的应力变化。具体布设位置如图9

    图  9  天然气管道前后土压力盒布设位置(单位:cm)
    Figure  9.  Location of earth pressure cell before and after natural gas pipeline (unit: cm)

    试验采用油压千斤顶施加水平荷载模拟滑坡推力,加载方法为慢速维持加载法分级加载[14],初始加载压力为1 MPa,每级加载压力增加0.5 MPa,试验一共设置7组加载工况。每级加载后观察桩顶百分表读数稳定后进行下级加载,具体加载工况见表3

    表  3  各级加载工况Q
    Table  3.  Q values of loading conditions at all levels
    工况加载压力/MPa工况加载压力/MPa
    11.053.0
    21.563.5
    32.074.0
    42.5
    下载: 导出CSV 
    | 显示表格

    图10荷载下桩顶水平位移数据分析可知,在各级水平荷载作用下,螺纹桩侧3#与4#群桩顶水平位移明显大于花管桩侧1#与2#群桩,在4 MPa加载压力作用下螺纹桩侧位移量为花管桩侧位移量的两倍之多,说明在相同水平推力荷载下花管桩抗滑承载力更强;在2 MPa以下水平荷载作用下桩顶位移变化微弱,2~3 MPa时桩顶位移开始有小幅度增长,当荷载达到3 MPa时,桩顶位移变化量出现转折,增长幅度明显加大。根据桩顶位移容许值为30~50 mm,以1.67 mm(相当于原型的50 mm)为作为试验模型桩顶位移容许值,螺纹桩侧桩顶位移量在水平荷载为2.5 MPa达到容许值,花管桩侧桩顶位移量在水平荷载为3 MPa达到容许值,表明在本次试验条件下花管微型桩的加固效果优于螺纹微型桩。

    图  10  桩顶水平位移随施加荷载的变化曲线
    Figure  10.  The horizontal displacement of pile top varies with the applied load

    根据图11可以看出,除个别测点外,在加载时段微型桩山侧土压力变化呈阶梯状上升,反映了随着加压荷载的增大,微型桩靠山侧土体应力在不断增加,并且有明显分级现象。

    图  11  山侧花管桩和螺纹桩土压力时程曲线对比
    Figure  11.  Comparison of soil pressure time-history curves of flowered pipe pile and threaded pile on the mountainside

    对比两侧不同桩体类型的土压力变化可知,花管桩侧不同埋深的土压力值普遍高于螺纹桩侧,花管桩侧在TH2测点达到最大值为46.09 kPa,相比螺纹侧在TL2测点达到的最大值20.54 kPa,花管桩侧土体最大应力高出螺纹桩侧两倍以上,结合螺纹桩桩顶最大位移与花管桩桩顶位移比值为2.4,说明花管桩侧土体受挤压程度大,分析为土拱效应[15]导致,在花管微型桩支护作用下桩后土体应力得不到有效释放,进一步表明花管桩的抗弯刚度高于螺纹桩。

    图11不同测点深度的土压力变化曲线可以看出,螺纹微型桩与花管微型桩的中上部位置土压力值最大,桩顶次之,桩底一般较小。

    图12,河侧螺纹桩测点4因土压力盒出现故障导致采集数据失真,故不作为分析对象。由图12可知,桩体河侧土压力变化趋势与山侧几乎保持一致,在加载阶段都有明显的上升趋势且各级加载土压力有明显的上升幅度加大的现象,卸载阶段开始下降并最终稳定在零值附近。

    图  12  河侧花管桩和螺纹桩土压力时程曲线对比
    Figure  12.  Comparison of earth pressure time-history curves of flowered pipe pile and threaded pile beside the river

    两侧桩体都在测点3(滑带附近位置)的最大级加载阶段达到峰值土压力,且螺纹桩侧峰值土压力(38.81 kPa)大于花管桩侧峰值土压力(32.57 kPa),高出比值约120%,对比山侧峰值土压力,花管桩河侧峰值土压力比山侧减少41%,螺纹桩河侧峰值土压力增加89%,由于抗滑桩承担并抵消了一部分滑坡推力,桩体河侧的土压力明显小于山侧,但花管桩两侧土压力差值明显大于螺纹桩两侧土压力差值,表明花管桩在承担滑坡推力作用方面发挥了良好的效果。

    提取桩体两侧各测点在各级加压荷载下土压力的峰值土压力进行单独分析,如图13绘制沿桩身深度不同位置随荷载改变的峰值土压力变化曲线分布图,峰值土压力基本随加压荷载的增大而增大,花管桩侧在4 MPa荷载下0.4 m桩深位置达到最大值46.09 kPa,螺纹桩侧在4 MPa荷载下0.4 m桩深位置达到最大值20.54 kPa。

    图  13  山侧花管桩和螺纹桩峰值土压力分布曲线对比
    Figure  13.  Comparison of peak earth pressure distribution curves of splined pipe pile and threaded pile on the mountainside

    花管桩侧峰值土压力沿桩深分布图大体呈“S”曲线形,且在各级荷载下分布规律保持一致,桩体中上部(位于滑带与桩顶中部)土压力值最大,在滑带附近土压力值有所减小,又在桩体中下部(位于滑带与桩底中部)有小幅度增大,往桩底位置开始减小。螺纹桩侧峰值土压力沿桩深分布图大体呈双“S”曲线形,同样在桩体中上部位置土压力值最大,滑带附近减小,桩体中下部土压力值最小,桩底土压力值开始增大。总的来说,花管桩侧土压力分布规律为桩中>桩顶>桩底,螺纹桩侧土压力分布规律为桩中>桩底>桩顶。

    滑面以上的土压力由推力荷载的传递引起,滑面以下的土压力主要由微型桩群的变形引起[16]。根据上述土压力分布规律,由此说明,花管桩在滑面以上桩中上部位置所受的推力荷载最大,滑面以下桩体中下部位置变形最大;螺纹桩在滑面以上桩中上部位置所受的推力荷载最大,滑面以下桩底位置变形最大。

    根据图14可知,花管桩侧在4 MPa荷载下0.65 m桩深位置达到最大值32.57 kPa,螺纹桩侧在4 MPa荷载下0.65 m桩深位置达到最大值38.81 kPa。

    图  14  河侧花管桩和螺纹桩峰值土压力分布曲线对比
    Figure  14.  Comparison of peak earth pressure distribution curves of flowered pipe pile and threaded pile beside the river

    花管桩河侧峰值土压力分布形式与山侧分布形式大体呈现相似,土压力大小分布规律仍为桩中>桩顶>桩底,但土压力最大值出现位置下移到滑面附近,说明在花管微型桩支护作用下推力荷载在土体的分布形式发生了改变,由于花管桩刚度大于桩周土体,土体应力传递到桩体上产生应力集中,桩后土体应力重分布,最大水平应力作用点下移。螺纹桩侧河侧峰值土压力相比山侧分布形式产生了较大差异,土压力大小分布规律变为桩中>桩顶>桩底,桩底的土压力相比山侧有很大幅度减小。与花管桩侧类似,由于桩前土体应力重分布,土压力最大值出现位置下移到滑面附近且比山侧土压力最大值高出89%,说明螺纹微型桩并没有有效承担推力荷载,变形较大,在推力荷载作用桩前土体应力仍较大。

    选取花管微型桩1号测试单桩与螺纹微型桩1号测试单桩作为研究对象,根据桩身两侧应变数据可由以下公式计算测点弯矩值[17]

    M=EI(ε1ε2)d (2)

    式中:M——测试截面的弯矩/(kN·m);

    E——桩身材料的弹性模量/MPa;

    I——测试界面的惯性矩/m4

    ε1——微型桩后的桩身应变;

    ε2——微型桩前的桩身应变;

    d——桩身直径/m。

    图1516为两种桩体各截面测点在分级加压荷载下的弯矩分布图,图中弯矩正值表示桩后侧受拉。

    图  15  花管桩实测桩身弯矩分布图
    Figure  15.  Measured bending moment distribution diagram of flowered pipe pile
    图  16  螺纹桩实测桩身弯矩分布图
    Figure  16.  Measured bending moment distribution diagram of threaded pile

    图15可以看出,在1~3.5 MPa加压荷载下,弯矩分布特征几乎保持一致,花管桩桩身弯矩沿深度方向呈“M”形分布,桩顶与桩底弯矩值较小,桩身中上部与中下部出现较大的负弯矩,桩身离模拟滑面以上5 cm位置处产生最大正弯矩;在4 MPa加载压力作用下,弯矩分布曲线有所偏移,桩身中上部位置负弯矩极具增大,最大正弯矩位置下移到桩中位置(模拟滑面处)且桩身正弯矩长度范围增大到0.5 m,滑面以下负弯矩最大值减小,且在桩底出现正弯矩。说明在1~3.5 MPa推力荷载下,花管桩桩体变形特征一致,桩后桩身中上部与中下部位置受压变形,桩后桩身离模拟滑面0~10 cm段受拉变形;在4 MPa推力荷载下,桩身弯曲变形加剧,在桩深0.4 m 处出现最大负弯矩−71.96 kN·m,在桩深0.8 m处(模拟滑面位置)出现最大正弯矩16.47 kN·m。花管桩设计时对桩身变形较大处可开展进一步的优化工作。

    图16可知,螺纹桩桩身弯矩分布沿深度方向呈“S”形,桩体正负弯矩位置在模拟滑面附近大致呈旋转对称分布,滑面以上大部分区段为负弯矩,滑面以下为正弯矩;螺纹桩在桩深0.4处出现最大负弯矩−64.44 kN·m,在桩深0.9m处(模拟滑面以下10 cm)出现最大正弯矩65.85 kN·m,相比花管桩,最大负弯矩相差不大,最大正弯矩高出近4倍,表明在相同推力荷载工况下,螺纹微型桩变形程度大于花管微型桩。

    为了研究天然气管道在两种不同微型桩支护作用下的变形特征,通过埋设土压力盒监测管道两侧土体应力变化,图17为管道山侧与河侧各测点的土压力变化时程曲线。其中TG1/TG1'测点位于花管桩支护侧,TG3/TG3'测点位于螺纹桩支护侧,TG2/TG2'测点位于管道接口位置两侧。

    图  17  管道山侧与河侧土压力时程曲线对比
    Figure  17.  Comparison of soil pressure time history curve between mountain side and river side of pipeline

    根据图17可以看出,管道山侧与河侧各测点土压力在加载阶段都有明显的随时间增大的趋势。山侧各测点变化趋势大致相同,在初期加载土压力值增长表现平稳,而在后期加载阶段土压力出现跳跃式增长,并在最后加载阶段达到峰值,位于螺纹桩支护侧的TG3测点峰值土压力达22.99 kPa,为花管桩支护侧的1.73倍;结合图18各级荷载下管道山侧峰值土压力分布曲线可知,管道山侧峰值土压力随荷载的增大而增大,并且螺纹桩支护侧峰值土压力整体大于花管桩支护侧及管中接口位置。说明花管桩在推力荷载下承载力表现优于螺纹桩,减小了传递到管道的滑坡推力,而螺纹桩在较大推力荷载下由于抗弯性能不足,变形严重,破坏后不能有效承担并抵消滑坡推力,传递到桩前管道的应力较大,从而导致管道易产生变形破坏。

    图  18  管道山侧峰值土压力分布图
    Figure  18.  Distribution diagram of peak earth pressure on the mountain side of pipeline

    图17,管道河侧土压力变化趋势与山侧存在较大差异,在加载阶段整体呈波动性增长,测点最大峰值土压力在螺纹桩支护侧为12.29 kPa,最小峰值土压力在花管桩支护侧为1.54 kPa,比值7.98∶1。结合图19管道河侧峰值土压力分布曲线,在分级推力荷载作用下,峰值土压力规律表现为:花管桩支护侧<管中接口位置<螺纹桩支护侧。

    图  19  管道河侧峰值土压力分布图
    Figure  19.  Distribution diagram of peak earth pressure at river side of pipeline

    图20分级荷载管道弯矩分布图可以看出,在较小推力荷载作用下(1~3 MPa),管道接口附近弯矩值整体较小,花管桩支护侧呈现负弯矩,螺纹桩支护侧呈现正弯矩,绝对值大小相差不大,说明在较小推力荷载下,管道变形程度较小;在较大推力荷载作用下(3.5~4 MPa),管道接口处弯矩急剧增大,最大值达−317.75 kN·m,说明在较大推力荷载下管道接口位置变形严重甚至破坏,在设计滑坡区管道工程时应注意管道接口位置的加固;另一方面,对比花管桩支护侧与螺纹桩支护侧管道弯矩可知,弯矩分布曲线明显向螺纹桩侧偏移,并整体大于花管桩支护侧,说明在本试验条件下花管微型桩支护作用下天然气管道的变形程度低于螺纹微型桩支护,在滑坡推力作用下 ,花管微型桩对管道的保护效益更突出,花管桩更适用于作为管道滑坡区域的支挡结构。

    图  20  管道实测桩身弯矩分布图
    Figure  20.  Measured pile bending moment distribution diagram of pipeline

    (1)花管微型桩山侧及河侧峰值土压力沿桩深分布形式基本相似,大体呈“S”曲线形,桩后土体土拱效应明显,且在各级荷载下分布形式大致保持一致,总体来说花管桩侧土压力分布规律为桩中>桩顶>桩底,滑带附近的桩体周围土压力较大,在抗滑桩设计工作中应重点考虑优化。

    (2)螺纹桩山侧峰值土压力沿桩深分布图大体呈双“S”曲线形,河侧峰值土压力相比山侧分布形式产生了较大差异,桩底的土压力相比山侧减小很大幅度;随外部荷载的增加桩周土压力增加幅度较大,表明螺纹微型桩在横向承载性能方面有所欠缺。

    (3)花管桩桩身弯矩沿深度方向呈“M”形分布,桩身离模拟滑面以上5 cm位置处产生最大正弯矩;螺纹桩桩身弯矩分布沿深度方向呈“S”形,桩体正负弯矩位置在模拟滑面附近大致呈旋转对称分布,滑面以上大部分区段为负弯矩,滑面以下为正弯矩;在相同推力荷载工况下,螺纹微型桩变形程度大于花管微型桩。

    (4)在滑坡作用下花管微型桩可以有效减小传递到管道的坡体应力,在一定程度上预防管道受力破坏;而螺纹桩在较大推力荷载下抗弯性能不足,变形严重,破坏后不能有效承担滑坡推力,传递到桩前管道的应力较大,从而导致管道变形程度更为强烈;在本试验条件下,花管微型桩对管道的保护效益突出,更适用于作为管道-滑坡区域的支挡结构。

  • 图  1   研究区概况图

    Figure  1.   Overview of the study area

    图  2   影响因子

    Figure  2.   Influencing factors

    图  3   不同分级策略的滑坡易发性建模及可解释性流程图

    Figure  3.   Flowchart of landslide susceptibility modeling and interpretability under different grading strategies

    图  4   因子共线性及平均重要性分析

    Figure  4.   Analysis of factor multicollinearity and average importance of influencing factors

    图  5   因子在不同数据集中的重要性,EI-Equal Interval等间距,NB-Natural Breaks自然断点

    Figure  5.   Importance of influencing factors in different datasets,EI-Equal Interval,NB-Natural Breaks

    图  6   不同分级条件下十折交叉验证的AUC

    Figure  6.   AUC values for ten-fold cross-validation under various grading conditions

    图  7   不同数据集ROC曲线

    注:fold:为交叉验证的数据集。

    Figure  7.   ROC curves for different datasets

    图  8   影响因子蜂群图

    Figure  8.   Bees swarm plot of influencing factors

    图  9   重要影响因子散点图

    Figure  9.   Scatter plots of key influencing factors

    图  10   研究区滑坡易发性图

    Figure  10.   Landslides susceptibility map of the study area

  • [1] 郭子正,何俊,黄达,等. 降雨诱发浅层滑坡危险性的快速评估模型及应用[J]. 岩石力学与工程学报,2023,42(5):1188 − 1201. [GUO Zizheng,HE Jun,HUANG Da,et al. Fast assessment model for rainfall-induced shallow landslide hazard and application[J]. Chinese Journal of Rock Mechanics and Engineering,2023,42(5):1188 − 1201. (in Chinese with English abstract)]

    GUO Zizheng, HE Jun, HUANG Da, et al. Fast assessment model for rainfall-induced shallow landslide hazard and application[J]. Chinese Journal of Rock Mechanics and Engineering, 2023, 425): 11881201. (in Chinese with English abstract)

    [2] 刘谢攀,殷坤龙,肖常贵,等. 基于I-D-R阈值模型的滑坡气象预警[J]. 地球科学,2022:1 − 15. [LIU Xiepan,YIN Kunlong,XIAO Changgui,et al. Meteorological early warning of landslide based on I-D-R threshold model[J]. Earth Science,2022:1 − 15. ( in Chinese with English abstract)]

    LIU Xiepan, YIN Kunlong, XIAO Changgui, et al. Meteorological early warning of landslide based on I-D-R threshold model[J]. Earth Science, 2022: 1 − 15. ( in Chinese with English abstract)

    [3]

    CUI Yulong,JIN Jiale,HUANG Qiangbing,et al. A data-driven model for spatial shallow landslide probability of occurrence due to a typhoon in Ningguo city,Anhui Province,China[J]. Forests,2022,13(5):732. DOI: 10.3390/f13050732

    [4] 黄发明,陈佳武,范宣梅,等. 降雨型滑坡时间概率的逻辑回归拟合及连续概率滑坡危险性建模[J]. 地球科学,2022,47(12):4609 − 4628. [HUANG Faming,CHEN Jiawu,FAN Xuanmei,et al. Logistic regression fitting of rainfall-induced landslide occurrence probability and continuous landslide hazard prediction modelling[J]. Earth Science,2022,47(12):4609 − 4628. (in Chinese with English abstract)]

    HUANG Faming, CHEN Jiawu, FAN Xuanmei, et al. Logistic regression fitting of rainfall-induced landslide occurrence probability and continuous landslide hazard prediction modelling[J]. Earth Science, 2022, 4712): 46094628. (in Chinese with English abstract)

    [5] 郭子正,殷坤龙,黄发明,等. 基于滑坡分类和加权频率比模型的滑坡易发性评价[J]. 岩石力学与工程学报,2019,38(2):287 − 300. [GUO Zizheng,YIN Kunlong,HUANG Faming,et al. Evaluation of landslide susceptibility based on landslide classification and weighted frequency ratio model[J]. Chinese Journal of Rock Mechanics and Engineering,2019,38(2):287 − 300. (in Chinese with English abstract)]

    GUO Zizheng, YIN Kunlong, HUANG Faming, et al. Evaluation of landslide susceptibility based on landslide classification and weighted frequency ratio model[J]. Chinese Journal of Rock Mechanics and Engineering, 2019, 382): 287300. (in Chinese with English abstract)

    [6] 黄发明,李金凤,王俊宇,等. 考虑线状环境因子适宜性和不同机器学习模型的滑坡易发性预测建模规律[J]. 地质科技通报,2022,41(2):44 − 59. [HUANG Faming,LI Jinfeng,WANG Junyu,et al. Modelling rules of landslide susceptibility prediction considering the suitability of linear environmental factors and different machine learning models[J]. Bulletin of Geological Science and Technology,2022,41(2):44 − 59. (in Chinese with English abstract)]

    HUANG Faming, LI Jinfeng, WANG Junyu, et al. Modelling rules of landslide susceptibility prediction considering the suitability of linear environmental factors and different machine learning models[J]. Bulletin of Geological Science and Technology, 2022, 412): 4459. (in Chinese with English abstract)

    [7] 黄发明,陈佳武,唐志鹏,等. 不同空间分辨率和训练测试集比例下的滑坡易发性预测不确定性[J]. 岩石力学与工程学报,2021,40(6):1155 − 1169. [HUANG Faming,CHEN Jiawu,TANG Zhipeng,et al. Uncertainties of landslide susceptibility prediction due to different spatial resolutions and different proportions of training and testing datasets[J]. Chinese Journal of Rock Mechanics and Engineering,2021,40(6):1155 − 1169. (in Chinese with English abstract)]

    HUANG Faming, CHEN Jiawu, TANG Zhipeng, et al. Uncertainties of landslide susceptibility prediction due to different spatial resolutions and different proportions of training and testing datasets[J]. Chinese Journal of Rock Mechanics and Engineering, 2021, 406): 11551169. (in Chinese with English abstract)

    [8] 曾韬睿,殷坤龙,桂蕾,等. 基于滑坡致灾强度预测的建筑物易损性定量评价[J]. 地球科学,2023,48(5):1807 − 1824. [ZENG Taorui,YIN Kunlong,GUI Lei,et al. Quantitative vulnerability analysis of buildings based on landslide intensity prediction[J]. Earth Science,2023,48(5):1807 − 1824. (in Chinese with English abstract)]

    ZENG Taorui, YIN Kunlong, GUI Lei, et al. Quantitative vulnerability analysis of buildings based on landslide intensity prediction[J]. Earth Science, 2023, 485): 18071824. (in Chinese with English abstract)

    [9] 杜国梁,杨志华,袁颖,等. 基于逻辑回归–信息量的川藏交通廊道滑坡易发性评价[J]. 水文地质工程地质,2021,48(5):102 − 111. [DU Guoliang,YANG Zhihua,YUAN Ying,et al. Landslide susceptibility mapping in the Sichuan-Tibet traffic corridor using logistic regression-information value method[J]. Hydrogeology & Engineering Geology,2021,48(5):102 − 111. (in Chinese with English abstract)]

    DU Guoliang, YANG Zhihua, YUAN Ying, et al. Landslide susceptibility mapping in the Sichuan-Tibet traffic corridor using logistic regression-information value method[J]. Hydrogeology & Engineering Geology, 2021, 485): 102111. (in Chinese with English abstract)

    [10] 闫举生,谭建民. 基于不同因子分级法的滑坡易发性评价——以湖北远安县为例[J]. 中国地质灾害与防治学报,2019,30(1):52 − 60. [YAN Jusheng,TAN Jianmin. Landslide susceptibility assessment based on different factor classification methods:A case study in Yuan’an County of Hubei Province[J]. The Chinese Journal of Geological Hazard and Control,2019,30(1):52 − 60. (in Chinese with English abstract)]

    YAN Jusheng, TAN Jianmin. Landslide susceptibility assessment based on different factor classification methods: A case study in Yuan’an County of Hubei Province[J]. The Chinese Journal of Geological Hazard and Control, 2019, 301): 5260. (in Chinese with English abstract)

    [11] 黄发明,曹中山,姚池,等. 基于决策树和有效降雨强度的滑坡危险性预警[J]. 浙江大学学报(工学版),2021,55(3):472 − 482. [HUANG Faming,CAO Zhongshan,YAO Chi,et al. Landslides hazard warning based on decision tree and effective rainfall intensity[J]. Journal of Zhejiang University (Engineering Science),2021,55(3):472 − 482. (in Chinese with English abstract)]

    HUANG Faming, CAO Zhongshan, YAO Chi, et al. Landslides hazard warning based on decision tree and effective rainfall intensity[J]. Journal of Zhejiang University (Engineering Science), 2021, 553): 472482. (in Chinese with English abstract)

    [12] 黄发明,曹昱,范宣梅,等. 不同滑坡边界及其空间形状对滑坡易发性预测不确定性的影响规律[J]. 岩石力学与工程学报,2021,40(增刊2):3227 − − 3240. [HUANG Faming,CAO Yu,FAN Xuanmei,et al. Influence of different landslide boundaries and their spatial shapes on the uncertainty of landslide susceptibility prediction[J]. Chinese Journal of Rock Mechanics and Engineering,2021,40(Sup 2):3227-3240. (in Chinese with English abstract)]

    HUANG Faming, CAO Yu, FAN Xuanmei, et al. Influence of different landslide boundaries and their spatial shapes on the uncertainty of landslide susceptibility prediction[J]. Chinese Journal of Rock Mechanics and Engineering, 2021, 40(Sup 2): 3227-3240. (in Chinese with English abstract)

    [13] 宋昭富,张勇,佘涛,等. 基于易发性分区的区域滑坡降雨预警阈值确定——以云南龙陵县为例[J]. 中国地质灾害与防治学报,2023,34(4):22 − 29. [SONG Zhaofu,ZHANG Yong,SHE Tao,et al. Determination of regional landslide rainfall warning threshold based on susceptibility zoning:a case study in Longling County of Yunnan Province[J]. The Chinese Journal of Geological Hazard and Control,2023,34(4):22 − 29. (in Chinese with English abstract)]

    SONG Zhaofu, ZHANG Yong, SHE Tao, et al. Determination of regional landslide rainfall warning threshold based on susceptibility zoning: a case study in Longling County of Yunnan Province[J]. The Chinese Journal of Geological Hazard and Control, 2023, 344): 2229. (in Chinese with English abstract)

    [14] 黄发明,叶舟,姚池,等. 滑坡易发性预测不确定性:环境因子不同属性区间划分和不同数据驱动模型的影响[J]. 地球科学,2020,45(12):4535 − 4549. [HUANG Faming,YE Zhou,YAO Chi,et al. Uncertainties of landslide susceptibility prediction:different attribute interval divisions of environmental factors and different data-based models[J]. Earth Science,2020,45(12):4535 − 4549. (in Chinese with English abstract)]

    HUANG Faming, YE Zhou, YAO Chi, et al. Uncertainties of landslide susceptibility prediction: different attribute interval divisions of environmental factors and different data-based models[J]. Earth Science, 2020, 4512): 45354549. (in Chinese with English abstract)

    [15] 仉文岗,何昱苇,王鲁琦,等. 基于水系分区的滑坡易发性机器学习分析方法:以重庆市奉节县为例[J]. 地球科学,2023,48(5):2024 − 2038. [ZHANG Wengang,HE Yuwei,WANG Luqi,et al. Machine learning solution for landslide susceptibility based on hydrographic division:case study of Fengjie County in Chongqing[J]. Earth Science,2023,48(5):2024 − 2038. (in Chinese with English abstract)]

    ZHANG Wengang, HE Yuwei, WANG Luqi, et al. Machine learning solution for landslide susceptibility based on hydrographic division: case study of Fengjie County in Chongqing[J]. Earth Science, 2023, 485): 20242038. (in Chinese with English abstract)

    [16] 杨得虎, 朱杰勇, 刘帅, 等. 基于信息量、加权信息量与逻辑回归耦合模型的云南罗平县崩滑灾害易发性评价对比分析[J]. 中国地质灾害与防治学报,2023,34(5):43 − 53. [YANG Dehu, ZHU Jieyong, LIU Shuai, et al. Comparative analyses of susceptibility assessment for landslide disasters based on information value, weighted information value and logistic regression coupled model in Luoping County, Yunnan Province[J]. The Chinese Journal of Geological Hazard and Control,2023,34(5):43 − 53. (in Chinese with English abstract)]

    YANG Dehu, ZHU Jieyong, LIU Shuai, et al. Comparative analyses of susceptibility assessment for landslide disasters based on information value, weighted information value and logistic regression coupled model in Luoping County, Yunnan Province[J]. The Chinese Journal of Geological Hazard and Control, 2023, 345): 4353. (in Chinese with English abstract)

    [17] 贾雨霏,魏文豪,陈稳,等. 基于SOM-I-SVM耦合模型的滑坡易发性评价[J]. 水文地质工程地质,2023,50(3):125 − 137. [JIA Yufei,WEI Wenhao,CHEN Wen,et al. Landslide susceptibility assessment based on the SOM-I-SVM model[J]. Hydrogeology & Engineering Geology,2023,50(3):125 − 137. (in Chinese with English abstract)]

    JIA Yufei, WEI Wenhao, CHEN Wen, et al. Landslide susceptibility assessment based on the SOM-I-SVM model[J]. Hydrogeology & Engineering Geology, 2023, 503): 125137. (in Chinese with English abstract)

    [18] 刘海知,徐辉,包红军,等. 基于集成学习的山区中小流域滑坡易发区早期识别优化试验[J]. 工程科学与技术,2022,54(6):12 − 20. [LIU Haizhi,XU Hui,BAO Hongjun,et al. Optimization experiment of early identification of landslides susceptibility areas in medium and small mountainous catchment based on ensemble learning[J]. Advanced Engineering Sciences,2022,54(6):12 − 20. (in Chinese with English abstract)]

    LIU Haizhi, XU Hui, BAO Hongjun, et al. Optimization experiment of early identification of landslides susceptibility areas in medium and small mountainous catchment based on ensemble learning[J]. Advanced Engineering Sciences, 2022, 546): 1220. (in Chinese with English abstract)

    [19] 曾韬睿,邬礼扬,金必晶,等. 基于stacking集成策略和SBAS-InSAR的滑坡动态易发性制图[J]. 岩石力学与工程学报,2023,42(9):2266 − 2282. [ZENG Taorui,WU Liyang,JIN Bijing,et al. Landslide dynamic susceptibility mapping based on stacking ensemble strategy and SBAS-InSAR[J]. Chinese Journal of Rock Mechanics and Engineering,2023,42(9):2266 − 2282. (in Chinese with English abstract)]

    ZENG Taorui, WU Liyang, JIN Bijing, et al. Landslide dynamic susceptibility mapping based on stacking ensemble strategy and SBAS-InSAR[J]. Chinese Journal of Rock Mechanics and Engineering, 2023, 429): 22662282. (in Chinese with English abstract)

    [20] 黄发明,陈彬,毛达雄,等. 基于自筛选深度学习的滑坡易发性预测建模及其可解释性[J]. 地球科学,2023,48(5):1696 − 1710. [HUANG Faming,CHEN Bin,MAO Daxiong,et al. Landslide susceptibility prediction modeling and interpretability based on self-screening deep learning model[J]. Earth Science,2023,48(5):1696 − 1710. (in Chinese with English abstract)]

    HUANG Faming, CHEN Bin, MAO Daxiong, et al. Landslide susceptibility prediction modeling and interpretability based on self-screening deep learning model[J]. Earth Science, 2023, 485): 16961710. (in Chinese with English abstract)

    [21]

    LUNDBERG S M,LEE S I. A unified approach to interpreting model predictions[C]//Proceedings of the 31st International Conference on Neural Information Processing Systems. December 4 - 9,2017,Long Beach,California,USA. ACM,2017:4768 − 4777.

    [22] 陈丹璐,孙德亮,文海家,等. 基于不同因子筛选方法的LightGBM-SHAP滑坡易发性研究[J/OL]. 北京师范大学学报(自然科学版),(2023-08-08)[2023-09-26] https://link.cnki.net/urlid/11.1991.N.20230808.1452.003. [CHEN Danlu,SUN Deliang,WEN Haijia,etal. A study on landslide susceptibility of LightGBMSHAP based on different factor screening methods[J]. Journal of Beijing Normal University (Natural Science),(2023-08-08)[2023-09-26]. (in Chinese with English abstract)]

    CHEN Danlu, SUN Deliang, WEN Haijia, etal. A study on landslide susceptibility of LightGBMSHAP based on different factor screening methods[J]. Journal of Beijing Normal University (Natural Science), (2023-08-08)[2023-09-26]. (in Chinese with English abstract)

    [23]

    DAHAL A,LOMBARDO L. Explainable artificial intelligence in geoscience:a glimpse into the future of landslide susceptibility modeling[J]. Computers & Geosciences,2023,176:105364.

    [24] 黄发明,曾诗怡,姚池,等. 滑坡易发性预测建模的不确定性:不同“非滑坡样本”选择方式的影响[J]. 工程科学与技术,2023,56(1):1 − 14. [HUANG Faming, ZENG Shiyi, CHI Yao, et al. Uncertainties of landslide susceptibility prediction modeling:influence of different selection methods of "non-landslide samples"[J]. Advanced Engineering Sciences,2023,56(1):1 − 14. ( in Chinese with English abstract)]

    HUANG Faming, ZENG Shiyi, CHI Yao, et al. Uncertainties of landslide susceptibility prediction modeling: influence of different selection methods of "non-landslide samples"[J]. Advanced Engineering Sciences, 2023, 56(1): 1 − 14. ( in Chinese with English abstract)

    [25] 罗路广,裴向军,崔圣华,等. 九寨沟地震滑坡易发性评价因子组合选取研究[J]. 岩石力学与工程学报,2021,40(11):2306 − 2319. [LUO Luguang,PEI Xiangjun,CUI Shenghua,et al. Combined selection of susceptibility assessment factors for Jiuzhaigou earthquake-induced landslides[J]. Chinese Journal of Rock Mechanics and Engineering,2021,40(11):2306 − 2319. (in Chinese with English abstract)]

    LUO Luguang, PEI Xiangjun, CUI Shenghua, et al. Combined selection of susceptibility assessment factors for Jiuzhaigou earthquake-induced landslides[J]. Chinese Journal of Rock Mechanics and Engineering, 2021, 4011): 23062319. (in Chinese with English abstract)

    [26] 宋宇飞,曹琰波,范文,等. 基于贝叶斯方法的降雨诱发滑坡概率型预警模型研究[J]. 岩石力学与工程学报,2023,42(3):558 − 574. [SONG Yufei,CAO Yanbo,FAN Wen,et al. Probabilistic early warning model for rainfall-induced landslides based on Bayesian approach[J]. Chinese Journal of Rock Mechanics and Engineering,2023,42(3):558 − 574. (in Chinese with English abstract)]

    SONG Yufei, CAO Yanbo, FAN Wen, et al. Probabilistic early warning model for rainfall-induced landslides based on Bayesian approach[J]. Chinese Journal of Rock Mechanics and Engineering, 2023, 423): 558574. (in Chinese with English abstract)

    [27]

    PROKHORENKOVA L,GUSEV G,VOROBEV A,et al. CatBoost:unbiased boosting with categorical features[C]//Proceedings of the 32nd International Conference on Neural Information Processing Systems. December 3 - 8,2018,Montréal,Canada. ACM,2018:6639–6649.

    [28] 高秉海,何毅,张立峰,等. 顾及In SAR形变的CNN滑坡易发性动态评估——以刘家峡水库区域为例[J]. 岩石力学与工程学报,2023,42(2):450 − 465. [GAO Binghai,HE Yi,ZHANG Lifeng,et al. Dynamic evaluation of landslide susceptibility by CNN considering InSAR deformation:A case study of Liujiaxia Reservoir[J]. Chinese Journal of Rock Mechanics and Engineering,2023,42(2):450 − 465. (in Chinese with English abstract)]

    GAO Binghai, HE Yi, ZHANG Lifeng, et al. Dynamic evaluation of landslide susceptibility by CNN considering InSAR deformation: A case study of Liujiaxia Reservoir[J]. Chinese Journal of Rock Mechanics and Engineering, 2023, 422): 450465. (in Chinese with English abstract)

    [29] 张俊,殷坤龙,王佳佳,等. 三峡库区万州区滑坡灾害易发性评价研究[J]. 岩石力学与工程学报,2016,35(2):284 − 296. [ZHANG Jun,YIN Kunlong,WANG Jiajia,et al. Evaluation of landslide susceptibility for Wanzhou district of Three Gorges Reservoir[J]. Chinese Journal of Rock Mechanics and Engineering,2016,35(2):284 − 296. (in Chinese with English abstract)]

    ZHANG Jun, YIN Kunlong, WANG Jiajia, et al. Evaluation of landslide susceptibility for Wanzhou district of Three Gorges Reservoir[J]. Chinese Journal of Rock Mechanics and Engineering, 2016, 352): 284296. (in Chinese with English abstract)

    [30]

    HUANG Faming,ZHANG Jing,ZHOU Chuangbing,et al. A deep learning algorithm using a fully connected sparse autoencoder neural network for landslide susceptibility prediction[J]. Landslides,2020,17(1):217 − 229. DOI: 10.1007/s10346-019-01274-9

    [31]

    XING Yin,CHEN Yang,HUANG Saipeng,et al. Research on the uncertainty of landslide susceptibility prediction using various data-driven models and attribute interval division[J]. Remote Sensing,2023,15(8):2149. DOI: 10.3390/rs15082149

    [32] 李文彬,范宣梅,黄发明,等. 不同环境因子联接和预测模型的滑坡易发性建模不确定性[J]. 地球科学,2021,46(10):3777 − 3795. [LI Wenbin,FAN Xuanmei,HUANG Faming,et al. Uncertainties of landslide susceptibility modeling under different environmental factor connections and prediction models[J]. Earth Science,2021,46(10):3777 − 3795. (in Chinese with English abstract)]

    LI Wenbin, FAN Xuanmei, HUANG Faming, et al. Uncertainties of landslide susceptibility modeling under different environmental factor connections and prediction models[J]. Earth Science, 2021, 4610): 37773795. (in Chinese with English abstract)

    [33] 方然可, 刘艳辉, 黄志全. 基于机器学习的区域滑坡危险性评价方法综述[J]. 中国地质灾害与防治学报,2021,32(4):1 − 8. [FANG Ranke, LIU Yanhui, HUANG Zhiquan. A review of the methods of regional landslide hazard assessment based on machine learning[J]. The Chinese Journal of Geological Hazard and Control,2021,32(4):1 − 8. (in Chinese with English abstract)]

    FANG Ranke, LIU Yanhui, HUANG Zhiquan. A review of the methods of regional landslide hazard assessment based on machine learning[J]. The Chinese Journal of Geological Hazard and Control, 2021, 324): 18. (in Chinese with English abstract)

    [34] 陈水满, 赵辉龙, 许震, 等. 基于人工神经网络模型的福建南平市滑坡危险性评价[J]. 中国地质灾害与防治学报,2022,33(2):133 − 140. [CHEN Shuiman, ZHAO Huilong, XU Zhen, et al. Landslide risk assessment in Nanping City based on artificial neural networks model[J]. The Chinese Journal of Geological Hazard and Control,2022,33(2):133 − 140. (in Chinese with English abstract)]

    CHEN Shuiman, ZHAO Huilong, XU Zhen, et al. Landslide risk assessment in Nanping City based on artificial neural networks model[J]. The Chinese Journal of Geological Hazard and Control, 2022, 332): 133140. (in Chinese with English abstract)

    [35] 阳清青, 余秋兵, 张廷斌, 等. 基于GDIV模型的大渡河中游地区滑坡危险性评价与区划[J]. 中国地质灾害与防治学报,2023,34(5):130 − 140. [YANG Qingqing,YU Qiubing,ZHANG Tingbin,et al. Landslide hazard assessment in the middle reach area of the Dadu River based on the GDIV model[J]. The Chinese Journal of Geological Hazard and Control,2023,34(5):130 − 140. (in Chinese with English abstract)]

    YANG Qingqing, YU Qiubing, ZHANG Tingbin, et al. Landslide hazard assessment in the middle reach area of the Dadu River based on the GDIV model[J]. The Chinese Journal of Geological Hazard and Control, 2023, 345): 130140. (in Chinese with English abstract)

    [36] 刘甲美,王涛,杜建军,等. 四川泸定MS6.8级地震诱发崩滑灾害快速评估[J]. 水文地质工程地质,2023,50(2):84 − 94. [LIU Jiamei, WANG Tao, DU Jianjun, et al. Emergency rapid assessment of landslides induced by the Luding MS6.8 earthquake in Sichuan of China[J]. Hydrogeology & Engineering Geology,2023,50(2):84 − 94. (in Chinese with English abstract)]

    LIU Jiamei, WANG Tao, DU Jianjun, et al. Emergency rapid assessment of landslides induced by the Luding MS6.8 earthquake in Sichuan of China[J]. Hydrogeology & Engineering Geology, 2023, 502): 8494. (in Chinese with English abstract)

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