Exploring mechanism of hidden,steep obliquely inclined bedding landslides using a 3DEC model:A case study of the Shanyang landslide in Shaanxi Province,China

Catastrophic geological disasters frequently occur on slopes with obliquely inclined bedding structures(also referred to as obliquely inclined bedding slopes),where the apparent dip sliding is not readily visible.This phenomenon has become a focal point in landslide research.Yet,there is a lack of studies on the failure modes and mechanisms of hidden,steep obliquely inclined bedding slopes.This study investigated the Shanyang landslide in Shaanxi Province,China.Using field investigations,laboratory tests of geotechnical parameters,and the 3DEC software,this study developed a numerical model of the landslide to analyze the failure process of such slopes.The findings indicate that the Shanyang landslide primarily crept along a weak interlayer under the action of gravity.The landslide,initially following a dip angle with the support of a stable inclined rock mass,shifted direction under the influence of argillization in the weak interlayer,moving towards the apparent dip angle.The slide resistance effect of the karstic dissolution zone was increasingly significant during this process,with lateral friction being the primary resistance force.A reduction in the lateral friction due to karstic dissolution made the apparent dip sliding characteristics of the Shanyang landslide more pronounced.Notably,deformations such as bending and uplift at the slope’s foot suggest that the main slide resistance shifts from lateral friction within the karstic dissolution zone to the slope foot’s resistance force,leading to the eventual buckling failure of the landslide.This study unveils a novel failure mode of apparent dip creep-buckling in the Shanyang landslide,highlighting the critical role of lateral friction from the karstic dissolution zone in its failure mechanism.These insights offer a valuable reference for mitigating risks and preventing disasters related to obliquely inclined bedding landslides.

Landslide displacement prediction based on optimized empirical mode decomposition and deep bidirectional long short-term memory network

There are two technical challenges in predicting slope deformation.The first one is the random displacement,which could not be decomposed and predicted by numerically resolving the observed accumulated displacement and time series of a landslide.The second one is the dynamic evolution of a landslide,which could not be feasibly simulated simply by traditional prediction models.In this paper,a dynamic model of displacement prediction is introduced for composite landslides based on a combination of empirical mode decomposition with soft screening stop criteria(SSSC-EMD)and deep bidirectional long short-term memory(DBi-LSTM)neural network.In the proposed model,the time series analysis and SSSC-EMD are used to decompose the observed accumulated displacements of a slope into three components,viz.trend displacement,periodic displacement,and random displacement.Then,by analyzing the evolution pattern of a landslide and its key factors triggering landslides,appropriate influencing factors are selected for each displacement component,and DBi-LSTM neural network to carry out multi-datadriven dynamic prediction for each displacement component.An accumulated displacement prediction has been obtained by a summation of each component.For accuracy verification and engineering practicability of the model,field observations from two known landslides in China,the Xintan landslide and the Bazimen landslide were collected for comparison and evaluation.The case study verified that the model proposed in this paper can better characterize the"stepwise"deformation characteristics of a slope.As compared with long short-term memory(LSTM)neural network,support vector machine(SVM),and autoregressive integrated moving average(ARIMA)model,DBi-LSTM neural network has higher accuracy in predicting the periodic displacement of slope deformation,with the mean absolute percentage error reduced by 3.063%,14.913%,and 13.960%respectively,and the root mean square error reduced by 1.951 mm,8.954 mm and 7.790 mm respectively.Conclusively,this model not only has high prediction accuracy but also is more stable,which can provide new insight for practical landslide prevention and control engineering.

Development characteristics and failure modes of reactivated ancient landslides in the Sichuan–Tibet transportation corridor,China

The risk of reactivated ancient landslides in the Sichuan–Tibet transportation corridor in China is significantly increasing,primarily driven by the intensification of engineering activities and the increased frequency of extreme weather events.This escalation has resulted in a considerable number of fatalities and extensive damage to critical engineering infrastructure.However,the factors contributing to the reactivation and modes of destruction of ancient landslides remain unknown.Therefore,it is imperative to systematically analyze the developmental characteristics and failure modes of reactivated ancient landslides to effectively mitigate disaster risks.Based on a combination of data collection,remote sensing interpretation,and field investigations,we delineated the developmental attributes of typical ancient landslides within the study area.These attributes encompass morphological and topographic aspects,material composition,and spatial structure of ancient landslides.Subsequently,we identified the key triggers for the reactivation of ancient landslides,including water infiltration,reservoir hydrodynamics,slope erosion,and excavation,by analyzing representative cases in the study area.Reactivation of ancient landslides is sometimes the result of the cumulative effects of multiple predisposing factors.Furthermore,our investigations revealed that the reactivation of these ancient landslides primarily led to local failures.However,over extended periods of dynamic action,the entire zone may experience gradual creep.We categorized the reactivation modes of ancient landslides into three distinct types based on the reactivation sequences:progressive retreat,backward thrusting,and forward pulling–backward thrusting.This study is of great significance for us to identify ancient landslides,deepen our understanding of the failure modes and risks of reactivated ancient landslides on the eastern margin of the Tibetan Plateau,and formulate effective disaster prevention and mitigation measures.

Landslide distribution and sliding mode control along the Anninghe fault zone at the eastern edge of the Tibetan Plateau

Tibetan Plateau is known as the roof of the world.Due to the continuous uplift of the Tibetan Plateau,many active fault zones are present.These active fault zones such as the Anninghe fault zone have a significant influence on the formation of special geomorphology and the distribution of geological hazards at the eastern edge of the Tibetan Plateau.The Anninghe fault zone is a key part of the Y-shaped fault pattern in the Sichuan-Yunnan block of China.In this paper,high-resolution topographic data,multitemporal remote sensing images,numerical calculations,seismic records,and comprehensive field investigations were employed to study the landslide distribution along the active part of the Anninghe.The influence of active faults on the lithology,rock mass structures and slope stress fields were also studied.The results show that the faults within the Anninghe fault zone have damaged the structure and integrity of the slope rock mass,reduced the mechanical strength of the rock mass and controlled the slope failure modes.The faults have also controlled the stress field,the distribution of the plastic strain zone and the maximum shear strain zone of the slope,thus have promoted the formation and evolution of landslides.We find that the studied landslides are linearly distributed along the Anninghe fault zone,and more than 80%of these landslides are within 2–3 km of the fault rupture zone.Moreover,the Anninghe fault zone provides abundant substance for landslides or debris flows.This paper presents four types of sliding mode control of the Anninghe fault zone,e.g.,constituting the whole landslide body,controlling the lateral boundary of the landslide,controlling the crown of the landslide,and constituting the toe of the landslide.The results presented merit close attention as a valuable reference source for local infrastructure planning and engineering projects.

Strain-based design for buried pipelines subjected to landslides

Landslides are one of the key problems for stability analysis of pipelines in the western region of China where the geological conditions are extremely complicated. In order to offer a theoretical basis for the pipe-soil interaction, the general finite element program ABAQUS is used to analyze the distribution of pipe strain caused by landslide through which the pipeline passes. In this paper the Ramberg-Osgood constitutive equation is used to study the strain-based mechanical characteristics of pipelines. Different calculation schemas are designed by considering the change of spatial relationship between pipeline and landslide, and the change of D/t, diameter-thickness ratio of pipeline. The results indicate that the pipeline is primarily subjected to tension stress when the landslide crosses the pipeline perpendicularly, the pipe strain is a maximum along the central axis of the landslide, and reverse bending occurs on pipeline at both edges of the landslide. The pipeline is primarily subjected to friction force caused by the downward movement of the landslide, and the friction force is relatively small when the landslide is parallel to the pipeline. The pipe strain is in proportional to D/t, and this means decreasing D/t can help to improve security of pipelines subjected to the landslide.

基于时序分析及CNN-BiLSTM-AM的阶跃型滑坡位移预测

传统基于递归神经网络的模型对阶跃型滑坡位移预测能力不足,为解决这一问题,提出一种基于时序分析及卷积神经网络-双向长短期记忆-注意力机制(CNN-BiLSTM-AM)的滑坡位移动态预测模型。首先使用变分模态分解方法(VMD)将序列分解为趋势项、周期项和随机项。采用二次指数平滑法拟合趋势项位移,然后引入最大互信息系数法(MIC)计算各类影响因子与周期项位移相关性,对于周期项和随机项位移采用CNN-BiLSTM-AM混合模型进行多因素和单因素预测,最终累加各分量预测值得到累积位移预测结果。实验结果表明,所提方法在最终累计位移预测结果中拟合系数R~2达0.984和0.987,平均绝对误差(MAE)分别为5.334和3.947,均方根误差(RMSE)分别为6.196和4.941,相比卷积神经网络-长短期记忆(CNN-LSTM)、麻雀搜索算法-核极限学习机(SSA-KELM)和NARX方法,所提方法能够更好的捕捉监测数据的时间相关性,预测精度显著提高,可为阶跃型滑坡预警及防治工作提供参考。

降雨诱发古滑坡多期复活机制物理模拟试验研究

雅砻江上游某拟建水电站的库区巨型滑坡近年来变形显著,对水电站的施工、运营构成严重威胁。在现场地质调查和变形分析的基础上,配制不同相似材料,采用底摩擦试验对滑坡的变形机制以及破坏模式进行研究。底摩擦试验结果表明:滑坡B1区的后缘裂缝形成的时间较前缘裂缝的形成时间要早,裂缝规模也较大,这与现场调查、访问信息一致。B1区监测点表明,后缘变形程度较前缘更大,符合推移式的变形模式,但若B1区发生破坏失稳,可能造成后缘的A区局部渐进式的失稳破坏,整体上(A+B1区)可能演化为牵引式的变形破坏模式。底摩擦试验对周家滑坡的变形破坏模式研究成果可为工程治理决策提供一定的依据。

基于EMD分解和改进GWO-BP神经网络的滑坡位移预测

考虑到滑坡位移受多因素的影响,结合信号分解与智能算法,提出了一种时序分解-模型构建-模型训练的EMD-BP-TIGWO滑坡位移预测方法。首先,利用EMD方法将滑坡监测数据分解为多个IMF分量及一个残余量,将分解后的分量划分为周期项及趋势项位移;其次,构建BP-TIGWO模型,引入Tent映射及自适应权重,提高灰狼算法的收敛速度及全局搜索能力,并利用TIGWO算法优化BP神经网络的权值及阈值;利用Pearson相关系数对周期项滑坡位移与降雨量间的时滞期数进行分析,利用优化后的BP模型分别对周期及趋势项滑坡位移进行预测;最后,将各分量预测值进行叠加得到滑坡累计位移预测值,并对模型预测准确率进行评价。实验结果表明,EMD-BP-TIGWO模型在考虑降雨输入特征下,连续32 d预测的RMSE、MAE及R2分别为0.64、0.51及0.97,模型预测精度明显高于未考虑时滞的EMD-GWO-BP、EMD-GWO-BP、BP-TIGWO、BP模型的预测精度,可为预测滑坡的位移提供参考。

基于历史坡面位移监测数据的滑坡变形预测

根据最优加权组合理论,结合多个适应不同曲线特征的数学模型,建立最优加权组合模型用于滑坡变形的预测。基于实际滑坡监测数据分析,对加权组合模型及单一模型在数据拟合精度及预测精度方面进行了分析比较。拟合数据的结果表明,加权组合模型的拟合精度最高,预测值与实际值比较吻合,因此加权组合模型可提高滑坡变形临近时段的预测精度,预测数据可用于滑坡的临滑预警。

面向滑坡监测预警的多参数耦合学习模型研究

对滑坡类型的地质灾害进行监测预警,是油气管道沿线地质灾害监测领域中的重要工作。然而传统监测预警大多根据经验设定阈值,缺少对监测数据的分析和提炼,容易造成阈值设定过高或过低的情况。为了解决此类问题,通过对监测数据的分析,基于高斯混合模型(Gaussian Mixture Model,缩写为GMM)构建了一种新的滑坡预警模型。首先,在数据异常值剔除、缺失值补齐的基础上,使用主成分(Principal Component Analysis,缩写为PCA)分析方法提取滑坡各监测数据变量特征,形成新维度的空间特征变量,其次使用GMM并结合变量特征构建了多参数耦合的滑坡预警模型,最后将该模型在滑坡监测真实数据上进行应用,从而实现对预警模型的验证。应用结果表明该模型能够提取滑坡监测数据的复合高级特征,实现对滑坡滑动的预警。

Failure mode and dynamic response of a double-sided slope with high water content of soil

A double-sided slope with high water content in sandy clay was considered under the action of seismic load. Its failure mode and dynamic response were investigated using a hydraulic servo shaking table test. The typical characteristic of failure mode and dynamic responses of the double-sided slope were analyzed. Experimental results show that slope failure undergoes a process of progressive deformation. The slope failure mode can be explained as creep sliding landslide. AFA(Amplification Factor of Acceleration) at the surface and inner parts of the slope shows an increasing trend with the increase of relative elevation. The relationship between AFA and EAA(Excitation Amplitude of Acceleration) is nonlinear. An empirical formula is proposed to describe preferably the relationship between AFA,relative elevation and dimensionless EAA. The AFA at the middle and upper parts of the slope increases apparently with increasing EFA(Excitation Frequency of Acceleration).

汶川大地震同震滑坡复活变形特征研究

同震滑坡复活具有不确定性、隐蔽性强和区域分布面积广等特点,往往被忽视,容易成为防灾减灾的盲区。该文根据同震滑坡堆积物变形复活所处的坡体位置和主要诱发因素,将复活类型划分为坡脚侵蚀型和坡面降雨型,基于InSAR技术、无人机多期航拍和地面调查数据,分别对青林沟滑坡和沙坝村滑坡变形特征、过程、原因进行分析,坡脚侵蚀型主要以“沟道侵蚀—坡体滑塌—坡体后退”模式变形和复活;坡面降雨型则以“颗粒运移—下渗增强—颗粒粗化—滑面发展”模式变形和复活。将InSAR技术应用于同震滑坡大范围和长时间序列变形监测,不仅是同震滑坡变形和复活研究的有效手段,对于地质灾害评价、预测与防治也具有参考意义。

基于ICEEMDAN分解和MHA-LSTM神经网络的滑坡位移预测

深度学习在机器视觉等各个领域的兴起,使长短时间记忆(LSTM)神经网络也越来越常用于滑坡预测工作中。为加强对历史信息的提取与捕捉,提出一种多头自注意力长短时间记忆(MHA-LSTM)神经网络用来预测滑坡位移。首先采用改进版的自适应噪声完备集合经验模态分解(ICEEMDAN)算法将滑坡位移分解成趋势项、周期项和噪声项;然后采用MHA-LSTM神经网络拟合带噪声的滑坡位移趋势项和周期项,两者相加即可得到滑坡总位移的预测结果;最后以白水河滑坡实测数据为例,考虑月降雨量和库水位因素的影响,对该滑坡位移的周期项和趋势项进行预测,并与其他传统神经网络的预测结果进行对比。结果表明:MHA-LSTM神经网络模型相较于其他传统神经网络模型,能更好地捕捉历史的信息特征,预测效果更佳,可为滑坡预测提供新的技术方法。

堰塞坝渗流稳定性及坝体动力响应研究

堰塞坝是由崩塌、滑坡等堵塞河道而形成的天然坝体。区别于人工坝,堰塞坝具有坝体结构松散、材料分布不均的特点,其一旦形成,极易在短时间内发生失稳破坏(漫顶溢流、渗流破坏及坝坡失稳),威胁下游群众的生命财产安全。堰塞坝的失稳破坏主要受到上游水位、坝体渗流及余震作用的影响,本文以唐家山堰塞坝为研究背景,基于通用有限元软件ABAQUS建立堰塞坝渗流及动力响应分析模型,研究堰塞坝在渗流及余震作用下的稳定性,并进一步分析渗流及坝体余震对堰塞坝溃坝模式的影响。研究结果表明,(1)唐家山堰塞坝坝体发生渗透破坏的可能性较小,但随着上游水位的上升,堰塞坝发生渗流破化的概率增大;(2)堰塞坝内部动力响应特征表现为沿坝高从上到下,坝体的加速度放大倍数逐渐减小,动力响应呈现明显的“表面放大”效应;(3)余震作用下,堰塞坝不会发生整体失稳破坏,但余震使得堰塞坝坝高降低,从而加速其发生漫顶溢流破坏。

阶跃型滑坡综合变形预测及监测预警方法研究

大型库岸滑坡的长期变形在汛期降雨作用下呈现明显的周期性“阶跃式”陡增特征。针对阶跃型滑坡的变形特征,本文提出了一种多源数据“融合-预测-预警”的三步式滑坡监测预警方法:(1)“融合”,即基于经验模态分解法将多点位移监测数据分别分解为趋势项和周期项,采用加权值法分别融合不同监测点的趋势项和周期项位移得到融合趋势项和融合周期项序列,并将两者叠加得到滑坡体的现状综合变形时间序列;(2)“预测”,即引入“一个预测周期”概念,采用滑动多项式拟合法和随机森林算法分别对融合趋势项和融合周期项进行预测并叠加得到滑坡体的预测综合变形时间序列;(3)“预警”,即基于斜率变点分析方法搜索综合变形曲线的“稳定点”和“跃迁点”,确定稳定变形和加速变形区间的斜率,建立阶跃型滑坡的四级递进式分级预警模型,基于该预警模型对滑坡现状进行预警。以向家坝水库某滑坡体自动化位移监测数据为研究对象,采用本文所提方法对该滑坡进行了综合变形预测和监测预警,结果表明:综合变形时间序列可以整体反映滑坡的变形演化规律,且预测结果可靠,根据分级预警模型判断此滑坡体当前处于稳定变形阶段(Ⅰ级预警)。

人类活动影响高速公路滑坡的发灾机理和致灾模式研究--以余家海滑坡成因机理为例

高速公路桥梁横跨滑坡体导致公路安全运营问题突出,经济损失严重。以余家海互通立交BK0+601桥滑坡体为例,通过现场调研和钻孔调查,分析该类型滑坡的发灾机理和致灾模式,计算在不同工况下边坡稳定性,提出该类型滑坡的针对性治理方案。结果表明,导致该滑坡发灾的主要因素为不合理的堆载与边坡前缘开挖后支护不及时的人类活动,在降雨的耦合作用下导致滑坡发生。揭示该滑坡致灾机理,是由于前缘水库清淤导致临空面增加,在滑坡的牵引-拉裂作用导致承灾体受损。降雨与库水位波动耦合加剧滑坡的灾变,使桥墩发生位移,威胁行车安全。致灾模式包括排水槽损坏、桥墩基础拉裂以及上部结构破坏和堵河次生灾害。通过稳定性计算与分析表明该滑坡为欠稳定,未来在降雨作用下会出现局部滑动。提出格宾挡墙与护坦的针对性治理措施,能防止库水波动影响,起到拦挡作用。

基于多因素-多尺度分析的阶跃型滑坡位移预测

为了定量分析阶跃型滑坡位移与诱发因素之间的时滞效应,提高位移预测精度,本文提出新的预测模型并进行对比分析。首先基于时间序列分析将滑坡累计位移分离为趋势项和周期项;然后采用最大信息系数(Cmi)、多元经验模态分解(MEMD)方法进行多因素分析和多尺度分析,构建出多因素-多尺度MEMD预测模型;最后以三峡库区八字门滑坡为例,通过Cmi选取最优滞后期的诱发因素作为模型输入,在用MEMD方法分解多元序列的基础上建立时间多尺度模型,并与单因素-单尺度模型、多因素-单尺度模型及单因素-多尺度EMD(经验模态分解)模型进行对比。结果表明:八字门滑坡降雨和库水位的最优滞后期分别为2 d和4 d;滑坡多元序列经MEMD方法分解后得到3组模态函数,每组均有7个分量,各对应分量的时间尺度一致,其中周期项位移受诱发因素的响应具有时间多尺度特性;多因素-多尺度MEMD预测模型的均方根误差相较于以上3种对比模型分别平均降低49.4%、36.9%和27.4%,平均绝对百分比误差分别平均降低38.0%、26.4%和15.8%。

强震作用下反倾薄层岩质斜坡倾倒破坏机制及动力响应研究

反倾岩质边坡在三江并流地区具有广泛的发育和分布,其变形和破坏现象在该区域内所有边坡问题中,尤为突出,地震是诱发滑坡的重要动力因素,常常导致大规模的滑坡灾害。以金沙江左岸宗绒历史性堵江滑坡为例,设计并完成了缩尺相似材料振动台物理模型试验。通过加载不同类型的地震波以及不同频率、幅值,研究反倾薄层岩质斜坡的动力响应和变形破坏机制以及软弱破碎带的影响。试验结果表明,反倾岩质斜坡在强震作用下存在高程放大效应和趋表效应,幅值越大越明显。斜坡加速度放大系数增长速率受频率的影响要高于幅值的影响,在不同幅值下,加速度放大系数最大值出现在0.2 g~0.3g。软弱破碎带的存在改变了斜坡动力响应特征,其厚度对地震波的放大效应存在明显的差异,为厚层段抑制,薄层段放大。幅值0.3g~0.4g为斜坡启裂的临界动力条件,0.7g~0.8g是斜坡失稳破坏的临界动力条件,其破坏过程大致可以分为3个阶段:①形成坡顶张拉裂缝和坡趾剪切裂缝;②裂缝的扩展和浅层块体的剪切破坏滑动-块状倾倒;③斜坡浅层的主滑面的形成,斜坡破坏。

Using Physical Model Experiments for Hazards Assessment of Rainfall-Induced Debris Landslides

Using physical simulation models, rainfall-induced landslides have been simulated under various rainfall intensities. During these simulations, we have monitored the physical and mechanical behaviors of the landslide over the slip surface at different heights of the model slopes, as well as taking the whole slope to identify its deformation and failure processes. The results show that the rainfall duration corresponding to the initiation of the debris landslide and is exponentially related to rainfall intensity. Corresponding to the three intervals of the rainfall intensity, there are three types of slope failure modes:(1) the small-slump failure at the leading edge of the slope;(2) the block-slump failure but sometimes there are large blocks sliding down;and(3) the bulk failure but sometimes there is the block-slump failure. Based on the total rainfall-lasting time and the associated proportion of failed mass volume, the early warning of debris landslide can be classified into five grades, i.e., red, orange to red, orange, yellow to orange and yellow, which correspond to the five slope failure modes, respectively.

基于希尔伯特边际谱理论的含隧道滑坡震动破坏模式研究——以隧道正交下穿主滑方向滑体为例

为探究地震作用下含隧道滑坡体的破坏模式,以正交下穿滑体主滑方向为例进行振动台试验,通过测试得到滑坡体内不同位置的加速度时程曲线,基于希尔伯特边际谱理论,获取不同位置测点的边际谱,并提取各测点边际谱峰值和特征频率,揭示隧道正交下穿滑体滑坡的破坏模式。结果表明:1)在不同强度地震作用下,基岩内地震能量有序传递,并不出现地震损伤,滑坡体内中上部坡体和下部坡体率先出现地震能量不能完整传递,出现地震损伤。2)隧道的存在劣化了上部坡体的力学环境,使得中下部坡体内隧道顶部T16测点在低能量输入下仍出现破坏。3)隧道正交下穿滑体主滑方向的震动破坏模式表现为崩落—拉裂—滑移破坏。