Constituents and features of displacement vector angle of colluvial landslide

Based on an analysis of displacement-monitoring data of Xintan landslide, China, it was discovered that the whole stability of large-scale colluvial landslides can be more accurately described by taking the displacement vector angle into account, and the stability trend of a slope can be evaluated and predicted using the displacement vector angle. This paper also further indicates that the monitored deformation vector angle can be divided into sliding deformation vector angle, plastic-deformation vector angle, compressive deformation vector angle and creep deformation vector angle in terms of the displacement properties of the slope. Through the analysis it is found that the displacement vector angle, for which there is no substitute, is an important parameter serving as an explicit criterion for the stability of the slope, and hence its significance in the prediction of landslides. It is also shown that as far as the displacement vector angle is concerned, there are different constituents and features in the different deformation stage of colluvial landslide. On the basis of these and using the principles of statistics, a discussion on the stability of the colluvial slopes is carried out in terms of the monitoring data of F-series points on Xintan slope. The analysis results coincided with the destabilized time and the sliding laws, thus demonstrating that the parameter of the displacement vector angle has to a certain extent, very important significance in the forecasting of landslides.     

降雨诱发型堆积层滑坡的位移动力学特征分析

根据降雨诱发型堆积层边坡物质组成特点与位移构成性质,建立了边坡粘弹性位移动力学模型与位移动力学方程,并以此为基础系统分析了其位移动力学行为和特征以及位移与地下水位或降雨量定量关系,发现了边坡的位移动力学特征主要取决于边坡的地下水位及其变化规律,且其位移和位移速度在整体失稳前具有强烈的波动性。上述位移动力学规律表明,仅运用位移或位移速度作为单一动力学响应参数来评价与预测该类边坡稳定性存在一定局限性。因此,选择和建立堆积层滑坡的位移多源信息预测理论与模型将具有重要的理论意义与实用价值。     

堆积层滑坡水动力位移耦合预测参数及其评价方法研究

在系统分析滑坡的物质组成和失稳动因的基础上,分析和研究了地下水在滑坡稳定性演化过程中的卸载与加载动力作用及其位移响应规律和特点。从非线性系统动力学角度,提出了运用地下水卸加载动力与位移响应耦合预测参数来评价边坡稳定性演化规律与失稳特征,即以地下水位变化量作为堆积层滑坡的卸加载动力参数,以相应的位移作为其卸加载响应参数,建立和确定了地下水卸加载动力与位移响应比预测参数与评价模型。同时,运用损伤力学基本原理,建立了其卸加载响应比与坡体损伤变量和稳定性系数的定量关系以及失稳判据。以三峡库区典型堆积层滑坡分析为例,运用地下水动力与位移耦合预测模型对其稳定性进行了分析与评价,发现地下水动力位移耦合预测参数变化与边坡稳定性实际动态演化规律基本吻合。研究成果表明,所确定的参数是水诱发型堆积层滑坡的一种有效位移动力评价参数,可运用该参数对该类滑坡的动态稳定性进行实时监测预警与评价。     

Field investigation of force and displacement within a strata slope using a real-time remote monitoring system

Research on monitoring and forecasting technology for slope stability is important for ensuring railway operation. This paper presents field investigation of force and displacement within a strata slope using a real-time remote monitoring system. Based on the interactions of the landslide body, the landslide bed and the monitoring anchor of slope, the mechanical principle of relative movement between the landslide body and the landslide bed can be found. This paper puts forward stress data obtained from a monitoring anchor as the main criterion for landslide stability. The stress will change continually inside the slope mass before the occurrence of a landslide. When the sliding force is larger than the anti-sliding force, deformation and landslides will occur; thus, the change in stress occurs before the change in displacement. In this study, the internal stress, deep displacement and surface strain of a railway slope were measured by a real-time remote-monitoring system, and a vibration metre was installed on the surface of the railway slope to study the influence of the train vibration load on the stability of the slope. The monitoring results are synthetically analysed temporally and spatially, then a railway slope forecasting model is proposed. According to the railway slope field application, the forecasting model makes successful predictions.     

堆积层滑坡临滑预报的新判据

研究表明，仅用速率大小作为滑坡的临滑判剧是不充分的。笔者在分析新滩堆积层滑坡位移矢量角动态特征的基础上，提出了堆积层滑坡临滑预报的新判据。     

Comparison on landslide nonlinear displacement analysis and prediction with computational intelligence approaches

Landslide displacement is widely obtained to discover landslide behaviors for purpose of event forecasting. This article aims to present a comparative study on landslide nonlinear displacement analysis and prediction using computational intelligence techniques. Three state-of-art techniques, the support vector machine (SVM), the relevance vector machine (RVM), and the Gaussian process (GP), are comparatively presented briefly for modeling landslide displacement series. The three techniques are discussed comparatively for both fitting and predicting the landslide displacement series. Two landslides, the Baishuihe colluvial landslide in China Three Georges and the Super-Sauze mudslide in the French Alps, are illustrated. The results prove that the computational intelligence approaches are feasible and capable of fitting and predicting landslide nonlinear displacement. The Gaussian process, on the whole, performs better than the support vector machine, relevance vector machine, and simple artificial neural network (ANN) with optimized parameter values in predictive analysis of the landslide displacement.     

Analysis of dynamic factors of debris landslide by means of the model of quantitative theory—using the Xintan landslide, China, as an example

If prediction of a debris landslide is to be completed, the first problem is how to determine and correlate the dynamic factors. The special composition and complicated geological conditions of debris landslides have many dynamic factors influencing displacement and stability. Correlations are generally very complicated. The relationship and sequence of factors is too complex to be completed by traditional mathematical model. In order to solve the problem above, a systematical study of the quantitative and qualitative dynamic factors using a model of the quantitative theory was performed based on the displacements of Xintan landslide, China. Rainfall was found to be the most important dynamic factor among the six factors controlling displacements. Relative importance sequence and correlation was also established. All prediction results agree with the mechanism and displacement pattern of the Xintan landslide. Optimized measures of prevention and control for debris landslides can be completed with correlation of the dynamic factors by means of the quantitative theory model.     

Modeling landslide recurrence in Seattle, Washington, USA

To manage the hazard associated with shallow landslides, decision makers need an understanding of where and when landslides may occur. A variety of approaches have been used to estimate the hazard from shallow, rainfall-triggered landslides, such as empirical rainfall threshold methods or probabilistic methods based on historical records. The wide availability of Geographic Information Systems (GIS) and digital topographic data has led to the development of analytic methods for landslide hazard estimation that couple steady-state hydrological models with slope stability calculations. Because these methods typically neglect the transient effects of infiltration on slope stability, results cannot be linked with historical or forecasted rainfall sequences. Estimates of the frequency of conditions likely to cause landslides are critical for quantitative risk and hazard assessments. We present results to demonstrate how a transient infiltration model coupled with an infinite slope stability calculation may be used to assess shallow landslide frequency in the City of Seattle, Washington, USA. A module called CRF (Critical RainFall) for estimating deterministic rainfall thresholds has been integrated in the TRIGRS (Transient Rainfall Infiltration and Grid-based Slope-Stability) model that combines a transient, one-dimensional analytic solution for pore-pressure response to rainfall infiltration with an infinite slope stability calculation. Input data for the extended model include topographic slope, colluvial thickness, initial water-table depth, material properties, and rainfall durations. This approach is combined with a statistical treatment of rainfall using a GEV (General Extreme Value) probabilistic distribution to produce maps showing the shallow landslide recurrence induced, on a spatially distributed basis, as a function of rainfall duration and hillslope characteristics.     

An analysis of storm-induced landslides in colluvial soils overlying mudrock sequences, southeastern Ohio, USA

In late-June, 1998, a series of thunderstorms dropped 16.5 cm (6.5 in.) of rain in a 72-h period over southeastern Ohio, causing extensive flooding, six deaths, and 179 million dollars in property damage. The storms also triggered more than 60 shallow landslides along a 64 km (40-mi) stretch of Interstate 77 between Buffalo and Marietta, Ohio. Almost all of the landslides were translational, occurring along the contact between colluvial soils and the underlying bedrock (shales, claystones, mudstones). Six sites were selected in the affected area for a detailed study of the shallow landslides. At each site, a record was made of the stratigraphy, slope geometry, number of landslides, landslide dimensions, hydrologic conditions, and influence of vegetation on landslide distribution. Both colluvial soils and underlying bedrock were sampled for laboratory investigations, which included determination of natural water content, grain size distribution, Atterberg limits, permeability, slake durability, and shear-strength parameters. Data from laboratory tests were used to perform sensitivity and stability analyses with respect to varying slope angles, strength parameters, and thicknesses of saturated colluvial soil. The results of the study indicate that the shallow landslides along Interstate 77 occurred when the colluvial soils reached 90% to 100% saturation, depending upon the slope angles.     

滑坡对降雨响应的多指标监测及综合预警探析:以赣南罗坳滑坡为例

降雨量和位移是当前降雨型滑坡监测预警最常用的指标。然而,降雨量和位移监测结果只能反映降雨作用下滑坡的变形情况,不能揭示滑坡内在物理力学性状对降雨的响应。因此,除降雨量和位移监测之外,建立包括体积含水率、基质吸力等反映滑坡动态演化过程的关键指标监测体系必将成为今后更真实地把握滑坡内在演化趋势、更准确地建立滑坡综合预警判据的最有效手段。笔者对赣南地区典型降雨型滑坡进行了多指标监测及综合预警示范研究。结果表明:(1)在降雨条件下滑坡土体内部体积含水率、基质吸力和温度等多指标均产生有规律的动态响应;(2)随着降雨的持续,滑体体积含水率与基质吸力的变化均具有显著的滞后现象;(3)体积含水率和基质吸力变化速率与滑体位移具有显著的正相关性;(4)滑体温度分布变化规律受大气温度和体积含水率的共同影响。以实测数据的滑坡稳定性分析为基准,在考虑实际降雨入渗深度与滑坡稳定性的关联度上,建立了包括日降雨量、体积含水率增加速率、基质吸力减小速率以及位移速度多元指标预警方法体系,提出了基于关键指标综合预警体系及确定方法,旨在为降雨滑坡准确预警提供新模式。     

Probabilistic forecasting of landslide displacement accounting for epistemic uncertainty: a case study in the Three Gorges Reservoir area,China

Accurate and reliable displacement forecasting plays a key role in landslide early warning. However, due to the epistemic uncertainties associated with landslide systems, errors are unavoidable and sometimes significant in traditional methods of deterministic point forecasting. Transforming traditional point forecasting into probabilistic forecasting is essential for quantifying the associated uncertainties and improving the reliability of landslide displacement forecasting. This paper proposes a hybrid approach based on bootstrap, extreme learning machine (ELM), and artificial neural network (ANN) methods to quantify the associated uncertainties via probabilistic forecasting. The hybrid approach consists of two steps. First, a bootstrap-based ELM is applied to estimate the true regression mean of landslide displacement and the corresponding variance of model uncertainties. Second, an ANN is used to estimate the variance of noise. Reliable prediction intervals (PIs) can be computed by combining the true regression mean, variance of model uncertainty, and variance of noise. The performance of the proposed hybrid approach was validated using monitoring data from the Shuping landslide, Three Gorges Reservoir area, China. The obtained results suggest that the Bootstrap-ELM-ANN approach can be used to perform probabilistic forecasting in the medium term and long term and to quantify the uncertainties associated with landslide displacement forecasting for colluvial landslides with step-like deformation in the Three Gorges Reservoir area.     

切割斜坡断层的几何形态对斜坡地震响应影响研究

与一般重力环境的滑坡相比,地震诱发滑坡在形成机理、运动特征等方面差异显著。天然和降雨条件下,断层破碎带作为边坡的不连续结构面,往往对斜坡的稳定性产生不利影响。而在地震作用下,边坡内部的断层破碎带存在一定的滤波作用,有可能减弱边坡的地震动响应。为了探究逆断层几何形态对边坡地震动响应的影响,以汶川地震中牛眠沟滑坡、窝前滑坡、谢家店子滑坡及东河口滑坡为研究对象,概化出断层切割型斜坡的地质模型,利用3DEC离散元软件对不同断层破碎带宽度、倾角、位置情况下的斜坡地震动响应进行模拟。模拟结果表明：（1）随着断层倾角增大,斜坡总位移峰值和坡面加速度峰值表现出增大的趋势,斜坡更易失稳;（2）坡顶监测点的峰值加速度一般大于坡底与坡腰的值,随着断层破碎带宽度增大,对斜坡地震动响应的直接影响愈加明显;（3）断层的存在会增加斜坡失稳的可能性,断层位于坡顶时斜坡地震动响应随断层倾角和破碎带宽度变化的规律性更明显。本研究可为深入揭示地震作用下断层破碎带对斜坡稳定性的影响提供理论依据。本研究可为深入揭示地震作用下断层破碎带对斜坡稳定性的影响提供理论依据。     

Monitoring tilting angle of the slope surface to predict loess fall landslide: an on-site evidence from Heifangtai loess fall landslide in Gansu Province,China

Landslides - Due to the huge losses caused by landslides, landslide deformation monitoring has become essential. The change of the tilting angle of the slope surface can reflect the instability...     

位移监测在边坡治理工程中的应用

位移监测可为分析滑坡的结构和成因、评价预测滑坡的稳定性及发展趋势等提供重要信息。实例分析表明,将位移监测资料与斜坡变形破坏现象结合起来,能准确地判定斜坡的变形阶段,评价和预测边坡施工及其使用过程中边坡的稳定状况。     

降雨诱发的浅表堆积层滑坡成因机理与稳定性预测模型

强降雨引起的滑坡多以浅表堆积层滑移为主,易群发、危害性大,其地下水和土体内部含水率对降雨的水文响应机制复杂,难以精准开展稳定性预测。为研究降雨引起的滑坡水文响应对稳定性的影响,以四川省青川县后山里滑坡为例,开展现场降雨入渗监测、相关性分析和力学分析。通过连续3年的降雨量、土体水分和地下水位等监测,分析降雨入渗-土体体积含水率-地下水位的响应规律,得到降雨量与地下水位的相关关系,并基于无限斜坡稳定性计算公式构建基于降雨量和地下水位埋深的浅层滑坡稳定性预测模型。结果表明：（1）年内地下水呈周期性波动,分为缓慢下降期、快速下降期和快速上升期三个阶段,且降雨量与地下水埋深呈线性负相关,与水位升幅相关性不显著;（2）根据稳定性预测模型确定了该滑坡失稳的临界降雨阈值为81.8 mm/d,地下水埋深阈值为0.73 m。研究结果可为降雨诱发浅表堆积层滑坡的预警预报提供参考。     

长江三峡新滩滑坡滑动机制

新滩滑坡是长江三峡大型滑坡之一,是由巨厚崩积物构成的一大型滑坡。强烈上升并有活动断层通过的峡谷斜坡是滑坡形成的特殊地质环境。上硬下软的地层组合为山体崩塌提供了有利条件。而丰沛的大气降水在滑坡活动过程中有着特殊重要的意义。降雨不仅淋滤细小粘土颗粒沉淀于风化页岩顶面,构成软弱滑带,而且常常是滑坡复活的诱发因素。     

滑坡位移的多模态支持向量机模型预测

将支持向量机（support vector machine,SVM）方法与信号分析中的经验模态分解（empirical mode decomposition, EMD）方法相匹配,提出了一种通过多模态支持向量机函数回归分析建模预测滑坡位移的理论方法。以边坡位移历史观测数据为基础,应用EMD方法获得滑坡形成过程中位移演化的几个特征时间模态,构成了多模态信息统计学习样本,确定了边坡位移演化的自适应多尺度变化信息。对应于每个经验模态的位移变化信息,引入了多模态SVM建模方法,然后合成不同经验模态下边坡位移的计算结果,得到滑坡位移的预测值。以卧龙寺新滑坡和新滩滑坡的监测数据为基础的理论预测结果表明,与采用遗传算法的神经网络方法的预测结果相比,支持向量机经验模态方法具有更强的预测能力,理论预测结果与实际监测值具有很好的一致性     

三峡库区典型顺斜向岩质滑坡变形破坏特征及失稳机制分析

近年来,三峡库区城集镇开发区顺斜向岩质滑坡失稳破坏现象时有发生,研究顺斜向岩质滑坡的变形破坏特征及失稳机制对防治此类滑坡具有重要意义。本文以巫山县白杨湾滑坡为例,通过现场踏勘、钻探和多种监测手段,对这一典型顺斜向岩质滑坡的变形破坏特征及失稳机制进行了深入研究。此滑坡所处地层为巴东组第二段泥岩,岩体破碎,地下水较丰富。滑坡岩层向右边界倾斜,右边界受断层控制,断层面与岩层面相交切割形成楔形体顺斜向滑移。滑坡体积约320×104 m3,滑动方向与岩层倾向夹角60°。受坡脚开挖和坡体建筑荷载等人类工程活动的影响,滑坡于2019年7月开始出现显著变形,滑坡中部的位移速率达到2～5 mm/d。2019年9月中旬,滑坡前部设置应急抗滑桩后,滑坡变形开始减缓至0～0.5 mm/d。白杨湾滑坡对城集镇开发区金科城造成巨大威胁,建议采取“搬迁避让+工程治理+专业监测”的防治对策。本文的研究成果对指导三峡库区顺斜向岩质滑坡防治和人工开挖诱发滑坡的防治具有重要借鉴意义。     

高陡矿山边坡山稳定性动态分析及其工程应用研究

露天开采形成的矿山边坡岩体的力学特性和稳定状态具有显著的动态特征,因此,传统的确定性分析方法难以获得可靠的稳定性分析结果。本文结合边坡岩体的变形监测信息,提出了矿山边坡稳定性动态分析技术。首先在边坡坡面布设一定数量的变形监测点,进行边坡变形监测。根据矿山实际开采情况(坡高、坡角和地质构造),建立对应于当前开采状况的数值分析模型,并基于监测位移与计算位移之差最小为准则,进行等效边坡岩体参数的模式识别。最后进行当前边坡的稳定性评价或未来开采解决的稳定性预测。实例研究结果显示,该分析技术能够获得与实际边坡稳定状态较为吻合的结果,为高陡矿山边坡的稳定性分析和安全评价提供一种新的研究方法。     

基于灰色最小二乘支持向量机的边坡位移预测

利用边坡实测位移序列预测边坡未来时间的位移,可以有效地判断边坡的稳定性。在分析了灰色预测方法和最小二乘支持向量机各自的优缺点的基础上,提出了将二者相结合的一种新的预测模型--灰色最小二乘支持向量机预测模型。新模型既发挥了灰色预测方法中“累加生成”的优点,弱化了原始序列中随机扰动因素的影响,增强了数据的规律性,又充分利用了最小二乘支持向量机求解速度快、易于描述非线性关系的优良特性,避免了灰色预测方法及模型存在的理论缺陷。同时,采用遗传算法进行了模型的参数优化,通过2个工程实例说明灰色最小二乘支持向量机模型预测边坡位移的有效性,具有较高的精度。