Stability characteristics of shallow landslide triggered by rainfall

Rainfall induced shallow landslides are known to be extremely dangerous since the sliding mass can propagate quickly and travel far from the source. Although the sliding mechanism in sloping ground is simple to understand, the problem may be complicated by unsaturated transient water flow. The flow behavior of rainwater in unsaturated sloping ground and the consequent factor of safety must be clearly understood to assess slope stability under rainfall conditions. A series of laboratory experiments was conducted to examine the critical hydrological states so that assessment of slope stability under rainfall condition can be performed. Based on the test results, a unique relationship between critical hydrological states, rainfall intensity, and soil properties was formulated. Sequential stability analysis provided insights into the stability of slopes subjected to variations in soil properties, slope angles and rainfall intensities, and the consequent variation in the depth of the failure plane, vital in landslide risk assessment, was determined through this analysis.The variation of rainfall intensity was found to strongly affect the depth of the failure plane in cohesionless sloping ground. Furthermore, the influence of rainfall intensity on the depth of the failure plane may be alleviated by a small magnitude of cohesive strength. The results of this study will reinforce knowledge of landslide behavior and help to improve mitigation measures in susceptible areas.

An Exploratory Analysis of Vegetation Strategies to Reduce Shallow Landslide Activity on Loess Hillslopes, Northeast Qinghai-Tibet Plateau, China

Heavy summer rainfall induces significant soil erosion and shallow landslide activity on the loess hillslopes of the Xining Basin at the northeast margin of the Qinghai-Tibet Plateau. This study examines the mechanical effects of five native shrubs that can be used to reduce shallow landslide activity. We measured single root tensile resistance and shear resistance, root anatomical structure and direct shear and triaxial shear for soil without roots and five root- soil composite systems. Results show that Atriplex canescens （Pursh） Nutt. possessed the strongest roots, followed by Caragana korshinskii Kom., Zygophyllum xanthoxylon （Bunge） Maxim., Nitraria tangutorum Bobr. and Lycium chinense Mill. Single root strength and shear resistance relationships with root diameter are characterized by power or exponential relations, consistent with the Mohr- Coulomb law. Root mechanical strength reflects their anatomical structure, especially the percentage of phloem and xylem cells, and the degree and speed of periderm lignifications. The cohesion force of root- soil composite systems is notably higher than that of soil without roots, with increasing amplitudes of cohesion force for A. canescens, C. korshinskii, Z. xanthoxylon, N. tangutorurn and L. chinense of 75.9%, 75.1%, 36.2%, 24.6% and 17.0 % respectively. When subjected to shear forces, the soil without root samples show much greater lateral deformation thanthe root-soil composite systems, reflecting the restraining effects of roots. Findings from this paper indicate that efforts to reduce shallow landslides in this region by enhancing root reinforcement will be achieved most effectively using A. canescens and C. korshinskii.

Modelling of shallow landslides with machine learning algorithms

This paper introduces three machine learning(ML)algorithms,the‘ensemble'Random Forest(RF),the‘ensemble'Gradient Boosted Regression Tree(GBRT)and the Multi Layer Perceptron neural network(MLP)and applies them to the spatial modelling of shallow landslides near Kvam in Norway.In the development of the ML models,a total of 11 significant landslide controlling factors were selected.The controlling factors relate to the geomorphology,geology,geo-environment and anthropogenic effects:slope angle,aspect,plan curvature,profile curvature,flow accumulation,flow direction,distance to rivers,water content,saturation,rainfall and distance to roads.It is observed that slope angle was the most significant controlling factor in the ML analyses.The performance of the three ML models was evaluated quantitatively based on the Receiver Operating Characteristic(ROC)analysis.The results show that the‘ensemble'GBRT machine learning model yielded the most promising results for the spatial prediction of shallow landslides,with a 95%probability of landslide detection and 87%prediction efficiency.

Dynamic assessment of rainfall-induced shallow landslide hazard

The assessment of rainfall-induced shallow landslide hazards is a significant issue in the Three Gorges Reservoir area in China due to the rapid development of land in the past two decades. In this study, a probabilistic analysis method that combines TRIGRS and the point-estimate method for evaluating the hazards of shallow landslides have been proposed under the condition of rainfall over a large area. TRIGRS provides the transient infiltration model to analyze the pore water pressure during a rainfall. The point-estimate method is used to analyze the uncertainty of the soil parameters, which is performed in the geographic information system(GIS). In this paper, we use this method to evaluate the hazards of shallow landslides in Badong County,Three Gorges Reservoir, under two different types of rainfall intensity, and the results are compared with the field investigation. The results showed that the distribution of the hazard map is consistent with the observed landslides. To some extent, the distributionof the hazard map reflects the spatial and temporal distribution of the shallow landslide caused by rainfall.

Automated machine learning for rainfall-induced landslide hazard mapping in Luhe County of Guangdong Province,China

Landslide hazard mapping is essential for regional landslide hazard management.The main objective of this study is to construct a rainfall-induced landslide hazard map of Luhe County,China based on an automated machine learning framework(AutoGluon).A total of 2241 landslides were identified from satellite images before and after the rainfall event,and 10 impact factors including elevation,slope,aspect,normalized difference vegetation index(NDVI),topographic wetness index(TWI),lithology,land cover,distance to roads,distance to rivers,and rainfall were selected as indicators.The WeightedEnsemble model,which is an ensemble of 13 basic machine learning models weighted together,was used to output the landslide hazard assessment results.The results indicate that landslides mainly occurred in the central part of the study area,especially in Hetian and Shanghu.Totally 102.44 s were spent to train all the models,and the ensemble model WeightedEnsemble has an Area Under the Curve(AUC)value of92.36%in the test set.In addition,14.95%of the study area was determined to be at very high hazard,with a landslide density of 12.02 per square kilometer.This study serves as a significant reference for the prevention and mitigation of geological hazards and land use planning in Luhe County.

Estimation of rainfall thresholds for shallow landslides in the Sierra Madre Oriental, northeastern Mexico

Landslides induced by prolonged rainfalls are frequent mass movements along the northeastern portion of the Sierra Madre Oriental in Mexico,causing significant damage to infrastructure.In this work,we have studied the connection between rainfall and landslides in the Santa Rosa Canyon,a catchment located in the northeastern Mexico.A landslide database triggered by major storms and hurricanes that have hit the region over the past 30 years was analyzed.A total of 92 rainfall events in the Santa Rosa Canyon were studied to determine the amount of precipitation needed to trigger shallow landslides.For each event the duration(D,in hours)and the cumulated rainfall event(E,in mm)were determined by using historical rainfall data from weather stations located near the study area.We have proposed an ED threshold for rainfall-induced landslides with durations 0.5<D<120 hours to address the conditions that trigger these events in the study area.On analyzing the obtained threshold,it has been established that almost 60 mm of a daily rainfall accumulation is required to trigger shallow landslides in the study area.This estimation is consistent with other calculations made for areas close to the Santa Rosa Canyon.Finally,we validated the predictive capability of the threshold with a different set of rainfall data that did not result in landslides performing a straightforward receiver operating characteristic analysis.A good approach was obtained,especially for rainfall events with daily measurements.Results could be used as input information in the design of a landslide early warning system for the northeastern Mexico,and replicated for other landslide prone areas in the region.

The spatial distribution characteristics of shallow fissures of a landslide in the Wenchuan earthquake area

Shallow fissures, being the main infiltration paths of fluid on the surface of a slope, played an important role in the whole process of a landslide. However, the spatial distribution characteristics of fissures in the slope are difficult to be determined. In this study, we attempted to characterize the variation pattern of slope fissures along depth in the Wenchuan earthquake area in Sichuan Province by combining engineering geological investigation, geomorphologic analysis and geophysical investigation. The geophysical methods that were used in this study include Multichannel Analysis of Surface Wave(MASW), Ground Penetrating Radar(GPR) and Electrical Resistivity Tomography(ERT). The results suggested that geophysical parameters(shear wave velocity, electromagnetic signals attenuation and resistivity) could provide valuable information for the spatial network of shallow fissures. Through the verification by engineering geological survey and geophysical sensitivity analysis, this work highlighted that MASW was the most appropriate technique to delineate the propagation of shallow fissures in a gravel soil slope.

Investigating the role of stratigraphy in large-area physically-based analysis of December 1999 Cervinara shallow landslides

The paper describes a large-area analysis of the triggering zones of shallow landslides on a case of unsaturated layered volcanic air-fall(pyroclastic) soil deposits in Cervinara site(18 km^2),Southern Italy.The physically-based model TRIGRS(Transient Rainfall Infiltration-Based Grid Regional Slope-Stability) is used,which is used with either saturated or unsaturated conditions and implemented in a GIS platform.In addition to using the TRIGRS model to simulate some recent landslides,a new simplified approach is also tested to take into account the actual layered soil stratigraphy.The consistency check of the model and of the input data is performed with reference to slope stable conditions before rainfall.The performances of the models are evaluated through the ROC curves and two other quantitative indexes taken from the literature referring to the slope failures caused by December 1999 rainstorm.Notwithstanding the simplifications and limitations of the present work,both unsaturated conditions and layered stratigraphy are outlined as key factors for the slope stability of shallow deposits of unsaturated coarse-grained soils subjected to short heavy rainfall.

Relationships between Landslide Types and Topographic Attributes in a Loess Catchment,China

Topographic attributes have been identified as the most important factor in controlling the initiation and distribution of shallow landslides triggered by rainfall.As a result,these landslides influence the evolution of local surface topography.In this research,an area of 2.6 km 2 loess catchment in the Huachi County was selected as the study area locating in the Chinese Loess Plateau.The landslides inventory and landslide types were mapped using global position system(GPS) and field mapping.The landslide inventory shows that these shallow landslides involve different movement types including slide,creep and fall.Meanwhile,main topographic attributes were generated based on a high resolution digital terrain model(5 m × 5 m),including aspect,slope shape,elevation,slope angle and contributing area.These maps were overlaid with the spatial distributions of total landslides and each type of landslides in a geographic information system(GIS),respectively,to assess their spatial frequency distributions and relative failure potentials related to these selected topographic attributes.The spatial analysis results revealed that there is a close relation between the topographic attributes of the postlandsliding local surface and the types of landslide movement.Meanwhile,the types of landslide movement have some obvious differences in local topographic attributes,which can influence the relative failure potential of different types of landslides.These results have practical significance to mitigate natural hazard and understandgeomorphologic process in thick loess area.

Identification of Soil, Rock and Tecto-Volcanism on Landslides in Tondano Watershed

This research was aimed to identify the soil, rock, and tecto-volcanism in their association with landslides intensity in Tondano watershed. The methods were survey method （soil, rock, and geomorphology）, joint data processing with stereonet 8, X-ray diffractometers for clay mineral identification, and earthquake data processing with GIS 10.2 Software. The magnitude of earthquake was 4-5.4 mb that resulted from tecto-volcanism activity. The earthquake caused the instability of soil and rock, especially in fault zones. The rock has been strong deformed with the highly developed intensity of fractures （advanced stage）. Soil dominated by Kaolinite and vermiculite minerals causes the instability conditions when it is saturated, while the nature of the bedrock with massive open fracture pattern causes the shear strength of the rocks decreases and on the contrary, the shear stress increases. Rainfall intensity is 73-145 mm/day that becomes a major factor of increased soil mass and burdening factor of the unstable rock. Slope is a factor that supports the intensity of mass movements of rock and soil in the form of shallow landslides.

裂隙优先流与风荷载耦合下台风诱发乔木斜坡浅层滑坡机制研究

在总结既有文献关于台风诱发滑坡特征的基础上,构建了裂隙优先流和台风风荷载耦合下乔木斜坡稳定性评价的简化力学模型。选取圆锥形、圆台形和圆柱形3种不同形状树冠的乔木,通过脉动风叠加平均风计算乔木不同高度处的风荷载时程曲线。利用ABAQUS有限元模拟获得台风作用下乔木的力学响应,并将乔木底部总力的最大值视为下滑力。以简化的二元结构斜坡为算例,在250 mm·d^(-1)的固定降雨强度下,计算了裂隙优先流(即裂隙斜坡)和风荷载耦合下发育3种类型乔木斜坡的稳定性,并与仅考虑风荷载时(即无裂隙斜坡)的结果进行比对。结果表明:由于裂隙优先流的存在,裂隙斜坡的雨水入渗速度明显快于无裂隙斜坡,其达到饱和状态所需的时间较无裂隙斜坡大幅缩短;裂隙斜坡和无裂隙斜坡的稳定性系数均随风速的增大而降低,整体上各风速下裂隙斜坡的稳定性系数明显小于无裂隙斜坡,且裂隙斜坡失稳时的风速要明显小于无裂隙斜坡;无论裂隙斜坡还是无裂隙斜坡,发育圆柱形树冠乔木时其稳定性系数下降程度最大,圆台形树冠次之,圆锥形树冠最小。研究结果可为台风等极端强对流天气下富乔木坡地浅层滑坡机制解析和预警预报研究提供参考。

降雨条件下植被根系对非饱和土浅层滑坡的加固作用

为了研究降雨条件下植被根系对非饱和土边坡浅层滑移的稳定性,基于可计算任意时刻任意土层深度处孔隙水压力分布与变化特征的降雨入渗解析模型,通过理论解析方法研究降雨入渗作用、植被根系抗拉强度、根面积指数以及根系生长方向等因素对边坡浅层滑移稳定性的影响。结果表明,植被根系的抗拉强度越大或根面积指数越大,则边坡的稳定性越强。随着边坡坡角的增大,植被根系对边坡稳定性的加固效应越加明显。此外,植被根系的生长方向也会对加固效果产生明显影响。在实际工程中,应在合理控制植被根系生长方向的基础上,选择根系发达且发育较长同时具有较大根面积指数的植被,以达到更好的护坡效果。

基于数据驱动的降雨型浅层滑坡易发性时空建模方法

传统滑坡易发性方法在计算空间概率时,通常忽视了影响因子的动态变化特征及地形单元之间的空间依赖关系。为了解决该难题,提出一种联合时空关系的广义加性模型,在江西省南部区域开展降雨型浅层滑坡易发性时空建模,预测给定时间范围内特定地形单元发生滑坡灾害的概率。首先,利用皮尔逊相关系数和基于赤池信息准则的序列前向特征选择方法对滑坡易发性影响因子进行评价与筛选;随后,联合时空关系构建伯努利广义加性时空模型开展易发性动态预测;最后,通过时空交叉验证方法评估模型的时空预测性能。结果表明,该模型具备优异的拟合性能和预测能力,在不同训练数据百分比下的预测性能非常稳定,其平均预测精度能够达到0.881。

裂隙优势流入渗诱发堆积层滑坡浅层破坏机理研究

土体裂隙为雨水提供了入渗通道,沿此通道形成的优势流主导着降雨入渗过程,导致滑坡稳定性逐步恶化,促使变形持续发展。以徐洼滑坡为例,结合地质勘察、现场宏观巡查以及监测数据等,在分析徐洼滑坡变形特征的基础上,阐明裂隙对滑坡浅层破坏的影响,利用数值模拟,分析不同深度裂隙引起的优势流入渗过程,在此基础上,通过无限边坡模型,计算优势流作用下浅层滑坡的稳定性演化特征,最终厘清优势流入渗诱发浅层破坏机理。研究表明:(1)裂隙提高了坡体的透水性,坡体变形与裂隙发育相互促进,而引起浅层破坏的往往是滑坡变形产生地表拉张裂隙的持续演化;(2)裂隙改变了雨水的入渗过程,一部分沿坡体表面向下垂直入渗,一部分沿裂隙入渗至裂隙末端向四周扩散,随着裂隙发育,降雨入渗由优势流主导;(3)滑坡变形与裂隙发育的关系密切,无裂隙时,坡体仅表层处于不稳定状态,表现为表层侵蚀破坏,随着裂隙演化,裂隙影响深度内坡体处于不稳定状态,表现为滑动破坏,裂隙末端的暂饱和区为最危险滑面。

黄土高原不同土地利用类型区浅层滑坡侵蚀特征分析——以蔡家川滑坡事件为例

近年来,黄土高原极端暴雨引发的滑坡、泥流等剧烈水土流失引起了广泛关注。2021年10月3—6日,山西省吉县蔡家川一带强降雨诱发了大面积浅层滑坡灾害。本文利用灾前灾后无人机影像和遥感图像、降雨数据及现场调查数据,对本次降雨过程、浅层滑坡起动条件和侵蚀特征进行了分析。结果表明:本次降雨为典型的“长历时、低雨强”类型,滑坡发生前的前期降雨历时达72 h,累积雨量达121.8 mm,滑坡集中发生时段的雨强达33.2 mm·(6h)^(-1)。与“短历时、高雨强”暴雨激发的滑坡相比,滑坡流动性相对较高。前期降雨是影响群发性滑坡灾害发生与流动性的重要因素。不同土地利用类型区滑坡起动地形条件差异显著,所需坡度与汇水面积均表现为:封禁林区>人工林区>农地区;地层岩性的渗透性也影响着浅层滑坡的起动,相同降雨量条件下,渗透性更好的林区发生浅层滑坡需要更高的面积坡度阈值。不同土地利用类型区,滑坡点密度、面密度与侵蚀强度的大小均表现为:农地区>人工林区>封禁林区,说明土地利用类型会在一定程度上影响滑坡的侵蚀程度,以乔木为主的林区更不利于滑坡发生。

浅层滑坡和沟谷型泥石流的关系研究:以福建省宝庄村2010年6月18日灾害为例

2010年6月14—18日,福建省顺昌县发生连续强降雨事件,致使多处地区发生滑坡、泥石流等群发性地质灾害,宝庄村是重灾区之一。在群发性滑坡灾区,往往很容易导致沟谷泥石流地发生。为了探索泥石流发生与滑坡物源之间的关系,选择了宝庄村43个流域,通过卫星遥感解译获取滑坡面积和流域面积的数据集,进而分析浅层滑坡诱发沟谷型泥石流的物源临界值,提出了一个经验模型来分析群发性滑坡引起的沟谷型泥石流的触发条件。结果表明,所提出的模型适用于福建省浅层滑坡诱发的沟谷型泥石流的物源分析。经验模型简单,输入的数据是容易测量的流域面积(A)和滑坡划痕面积(A_(0))。因此,该方法可以用于分析其他地区浅层滑坡诱发的沟谷型泥石流。

乡镇滑坡灾害精细化易发性评价——以万州区熊家镇为例

地质灾害精细化易发性评价是乡镇尺度地质灾害风险防控的基础,探究适应于乡镇级评价精度需求和精细化调查数据的易发性评价方法成为研究的重点。以万州区熊家镇为研究区,在精细化地质灾害调查的基础上,将滑坡灾害细分为厚层堆积层滑坡和浅层堆积层滑坡,分别对其进行斜坡单元划分,并基于灾害的成因机制构建差异化的易发性评价指标体系;针对乡镇尺度评价区灾害样本数量少、难以采用统计和机器学习类评价方法的问题,引入多分类逻辑回归模型进行小样本区的易发性评价;综合滑坡单元、非滑坡单元和存在一定变形、但尚未形成整体滑移的不稳定斜坡单元,突破传统的0、1二元变量,构建包含“中间态”变量的易发性评价灾害样本集,从而实现对灾害样本的有效扩充和对不同程度易发性的量化评价。结果表明:浅层堆积层滑坡高和极高易发区主要位于研究区北部顺向坡以及中部、南部的人类活动集中区域,且70.59%的调查变形点位于高和极高易发区;厚层堆积层滑坡极高和高易发区主要分布于西部坡度较缓的河流两岸,评价结果精度AUC值为0.823。本研究对乡镇及其同等尺度地质灾害风险调查与评价具有参考价值。

基于现场原型测试的乔木覆盖滑坡区根-土复合体力学性能研究

为揭示乔木根系力学特性及其对斜坡的力学效应,选取福建武平高植被覆盖区滑坡为研究点,对研究区典型乔木根系按径级分类后进行根系抗拉力学特性测试,并在滑坡现场进行不同根系横截面积比(rootcross-sectionalarearatio,简称RAR)、含水率下的根-土复合体原位直剪试验以及剖面根系分布特征调查,对根系在浅层滑坡的力学效应展开探究。研究结果表明:(1)1~7径级下马尾松、杉木抗拉力范围为12.45~673.09 N,抗拉力与根径呈幂函数正相关;抗拉强度范围为7.16~60.95MPa,抗拉强度与根径呈幂函数负相关。杉木根系平均抗拉力与抗拉强度均大于马尾松。(2)乔木根系显著提高了土体抗剪强度,根系对土体提供的附加黏聚力与剪切面RAR显著正相关。杉木根系构型更接近R型,马尾松为VH型,相近RAR下杉木对土体的加固作用优于马尾松。(3)随着含水率增加,马尾松、杉木根-土复合体抗剪强度显著下降,水分入渗使根系对土体提供的附加黏聚力受到削减。(4)基于Wu模型,考虑水分对土体黏聚力与根系附加黏聚力的影响,建立了考虑含水率的根-土复合体抗剪强度值估算模型,经检验,精度高于Wu模型,结果较为合理。(5)根系对浅层滑坡虽然具有加固作用,但受到根系分布深度、密度以及降雨等因素限制,强降雨作用下根系对浅层滑坡稳定性贡献具有局限性。

广东省暴雨型浅层滑坡灾害动力预警模型与气象风险预警研究

针对县级地质灾害气象风险预警面临的精度及模型建设问题,根据广东省地质灾害主要发生在坡面残坡积浅表层的突出特点,通过对典型地质灾害进行物理模拟试验和数值模拟,研究广东省浅表层斜坡失稳发生机理。研究表明:边坡在暴雨条件下,斜坡岩土体容易在浅表层首先造成失稳,影响因素主要有降雨量、降雨历时、土体类别和坡体结构等因素。由此,对研究区划分斜坡单元,按各斜坡单元的坡长、坡度、岩土类型、分层及其关键物理力学参数开展斜坡单元概化分类,并将Green-Ampt降雨入渗模型和无限边坡稳定性评价方法相结合,优化构建了动力学斜坡稳定性评价模型。结合龙川县贝岭镇流域应用实例,初步探索了坡面单元尺度下地质灾害气象风险预警斜坡失稳动力学预警技术,可为广东省开展以斜坡单元预警为主要方式的县级地质灾害气象风险预警提供支撑。

Spatial prediction of rainfall-induced shallow landslides using hybrid integration approach of Least-Squares Support Vector Machines and differential evolution optimization:a case study in Central Vietnam

This study represents a hybrid intelligence approach based on the differential evolution optimization and Least-Squares Support Vector Machines for shallow landslide prediction,named as DE-LSSVMSLP.The LSSVM is used to establish a landslide prediction model whereas the DE is adopted to search the optimal tuning parameters of the LSSVM model.In this research,a GIS database with 129 historical landslide records in the Quy Hop area(Central Vietnam)has been collected to establish the hybrid model.The receiver operating characteristic(ROC)curve and area under the curve(AUC)were used to assess the performance of the newly constructed model.Experimental results show that the proposed model has high performances with approximately 82%of AUCs on both training and validating datasets.The model’s results were compared with those obtained from other methods,Support Vector Machines,Multilayer Perceptron Neural Networks,and J48 Decision Trees.The result comparison demonstrates that the DE-LSSVMSLP deems best suited for the dataset at hand;therefore,the proposed model can be a promising tool for spatial prediction of rainfall-induced shallow landslides for the study area.