基于土工试验的滑坡特征参数求取分析及稳定性计算

为确保滑坡稳定性计算结果的准确性,结合滑坡现场地质条件,通过土工试验分区确定滑坡土体抗剪参数,并以参数求取结果开展滑坡稳定性计算。分析结果表明,以相应指标的试验结果为基础,滑坡后缘→滑坡中部→滑坡前缘的含水率逐步增加,液限、塑限逐步减小,黏聚力逐步增加,内摩擦角逐步减小。基于暴雨状态条件下的土工试验结果计算的稳定性系数近似于1,处于一种暂时稳定状态,与现场在暴雨状态下滑坡的稳定性状态是一致的,说明土工试验求取的滑坡特征参数是适用的。在相应计算剖面及计算工况条件下,传递系数法的稳定性系数均不同程度的小于极限平衡法的稳定性系数,即传递系数法的稳定性计算更加趋于不利;由于极限平衡法采用的是《建筑边坡工程技术规范》(GB 50330—2013)规定,其安全系数相对更大,所以极限平衡法的剩余下滑力相对更大,建议以极限平衡法的剩余下滑力开展滑坡后续防治设计。

万州区典型堆积层滑坡滑带土抗剪强度参数间关系研究

滑坡抗剪强度参数主要有天然峰值强度、饱和峰值强度、天然残余强度和饱和残余强度4种,这4种强度之间的关系目前还研究甚少。为此,提出了以收集的万州区20个典型堆积层滑坡147组抗剪强度参数为基础,采用数学分析软件SPSS筛选饱和残余强度的影响因素,然后利用Excel分析饱和残余强度与其影响因素的相关关系,以此判断影响因素的函数形式,在此基础上采用多元非线性回归模型研究饱和残余强度与其他3种抗剪强度参数之间的关系式。最后以万州区典型堆积层滑坡——螺丝冲滑坡为例,将多元非线性回归模型的计算结果与多元线性回归模型计算结果及试验值进行对比分析。研究表明,多元非线性回归模型计算的相对误差在8%以内,而多元线性回归模型计算的相对误差在6.85%~23.88%之间,多元非线性回归模型精度更高。

某水电站铅厂滑坡滑带参数取值研究

滑坡是一种重要的地质灾害,合理地治理滑坡是工程建设的关键,而合理的力学参数对滑坡治理又起到重要的作用,其中滑坡滑带力学参数是最关键参数之一。根据现场踏勘和地质资料分析,对滑坡滑带参数在含水条件和天然条件下进行反演分析、利用采集的数据为学习样本进行神经网络学习,对室内实验3组数据进行BP神经网络预测土样的C、Φ值,并参照已知滑坡滑带的力学参数来综合确定该滑坡滑带的力学参数,为合理治理该滑坡提供依据。

地震作用下滑坡稳定参数敏感性研究

本文重点探讨了等效线性化法和递推算法在滑坡地震机制作用下对稳定性影响参数的敏感性计算结果产生的影响,并列举了对滑坡稳定参数敏感性分析的众多方法,同时对该项研究的研究现状进行了归纳总结。

基于能量守恒原理滑石板岩质边坡滑坡速度分析

针对云南省金沙江两家人水电站滑石板边坡拉裂区岩体,考虑滑坡体下滑过程中与坡下已有堆积体发生冲击碰撞的作用,基于能量守恒的原理,依据滑坡体锁固段储能方式的不同,计算了滑坡遽发时的速度,并且依次分析了滑坡下滑各阶段速度和加速度的变化,得出了滑坡体下滑的最终速度;考虑到初始启滑速度及其与坡下堆积体的冲击碰撞作用在预测滑坡的速度及堆积规模方面不可忽略,可为其他滑坡速度的预测和滑坡后减灾防灾综合分析提供参考。

利用InSAR升降轨形变数据反演滑坡深度——以桃坪乡古滑坡为例

传统滑坡深度探测方法只能确定坡体部分稀疏单点的深度,无法准确反应整个滑坡体的深度分布情况,且探测成本高昂。针对该问题,本文发展了一种基于升降轨InSAR形变的滑坡深度反演方法,其基本流程是:首先利用时序InSAR技术分别获取升轨和降轨卫星视线向形变速度场,再根据卫星视线向与滑坡的空间几何关系,解算获得滑坡表面沿坡向和法向的二维形变场,进一步在质量守恒准则下构建基于坡面二维形变的滑坡深度反演模型,实现滑坡深度的反演计算。本文以四川省理县桃坪乡古滑坡体为研究对象,InSAR成果显示该滑坡运动面积~2.9 km^(2),流变参数取0.7时,反演滑坡深度集中分布在9~33 m,滑坡体积约3.49×10^(7)m^(3),结果与已有研究结论基本一致,证明了本文发展理论与方法的可靠性。且本文提出方法可获得滑坡体连续的深度数据,能更为直观地反映滑坡危险区域,可为滑坡灾害影响分析及灾害防治提供关键的数据支撑。

秭归水泥厂滑坡勘察

华新水泥股份有限公司秭归水泥厂滑坡勘察,结合滑坡区地质条件,分析产生滑坡的原因,提出治理滑坡的方案及建议。

贵州省开阳县花犁乡清江村滑坡稳定性初步研究

结合工程实例,通过天然条件下的滑坡稳定性以及库区蓄水后稳定性的对比,对库区滑坡稳定性进行了研究,从滑坡特征、力学特性、发展趋势等方面作了分析,为滑坡治理措施的制定提供了理论依据。

通过多时相机载激光雷达数据追踪和调查复杂活动滑坡 —Momaguto滑坡（意大利南部）

本文论述了对Montaguyo（意大利）活动滑坡的多时相激光雷达的研究。通过四个激光雷达分别获取了2006年5月、2009年7月、2010年4月、2010年6月的数字地形模型。通过对选定形态参数的解译（表面粗糙度、地形表面残差）以及这些参数时空变化的统计分析能够进行滑坡的再现和追踪。已经完成滑坡边界的监控，进行了隆起和沉降区、形变量和／或堆积物、垂直和水平位移的平均速率（退缩的平均速率和滑坡前端的推进速率）估算。对在不同时间影响滑坡的形变结构（陡坡、裂缝、褶皱）进行了制图；其中一些结构表征了滑坡即将进入不稳定过程的前兆或提供了结构位置相关信息。识别了不同的活动（如岩崩和泥石流）和几何形状（例如沟渠流动），检测了由人工排水和地面处理／清理工作引起的地形特征随时间的变化。我们根据激光雷达获得的信息能够解译滑坡的运动。研究结果表明，利用机载激光雷达能够提供重力．控制过程相关监测策略的新观点。

Three-dimensional CSAMT Forward Modeling of Potential Landslide Sliding Surfaces Using Finite Element Method

Landslides are a type of natural disaster that can cause substantial harm to humanity.Monitoring and predicting the initiation of potential landslides is critical to avoiding losses due to disasters and economic activities.The impact of the controlled-source audio-frequency magnetotelluric method on investigating landslide surfaces is assessed through numerical simulations with a finite element approach.A Dirichlet boundary condition is selected to match the truncated boundary,resulting in a remarkable improvement in simulation efficiency.Rederivation of the formulas for a layered medium adept to the controlled-source audiofrequency magnetotelluric method is necessary to determine the electromagnetic field at any location along the truncated boundary.After the reliability evaluation of the new codes,a landslide model with a slide surface is designed,and the characteristics of its electromagnetic field and the apparent resistivity are studied.Instead of the total electromagnetic field,which is strongly infl uenced by topography variation,the apparent resistivity should be used for sliding surface detection.The normalized pure anomalous electromagnetic field may also be employed to quickly assess the detectability of the sliding surface.Overall,this study demonstrates that the controlled-source audio-frequency magnetotelluric method can be employed for investigating landslides,and recommends survey parameters,including configuration,frequency range,and length of survey line in landslide exploration.

Large-scale Rock Landslide Slip Surface Location Analysis and Strength Parameters Evaluation

Large-scale rock landslides have huge impacts on various large-scale rock engineering and project operations. They are also important aspects evaluating geological disasters. In the initial evaluations on the stability of large-scale rock landslides, in most cases, it is difficult to conduct evaluation or to have accurate evaluations because most of large-scale rock landslides are huge in size, high in slopes, and located in the canyon of mountains, which makes the exploration very difficult and thus hard to get credible data on slip surface form, location, depth and strength. This paper describes the Badi landslide happened along the Lancang River, and systematically introduces methods to analyze and verify large-scale slip surface form using terrain conditions surrounding the large-scale landslide, shape of the slide walls, and development patterns of streams and gully. This paper also introduces ways to obtain strength parameters of slip surface with the soil in the slide zone by using the principles of stress state, principles of gravity compaction, structure regeneration and strength regeneration. It is confirmed that analyzed results to the slip surface are basically consistent with the exploration results. The methods introduced here have been successfully applied to evaluate the stability of Badi large-scale rock landslide and have been applied in engineering practices.

Progressive Modelling of the Gravity-induced Landslide Using the Local Dynamic Strength Reduction Method

The failure of slope is a progressive process, and the whole sliding surface is caused by the gradual softening of soil strength of the potential sliding surface. From this viewpoint, a local dynamic strength reduction method is proposed to capture the progressive failure of slope. This method can calculate the warning deformation of landslide in this study. Only strength parameters of the yielded zone of landslide will be reduced by using the method. Through continuous local reduction of the strength parameters of the yielded zone, the potential sliding surface developed gradually and evolved to breakthrough finally. The result shows that the proposed method can simulate the progressive failure of slope truly. The yielded zone and deformation of landslide obtained by the method are smaller than those of overall strength reduction method. The warning deformation of landslide can be obtained by using the local dynamic strength reduction method which is based on the softening characteristics of the sliding surface.

A back-propagation neural-network-based displacement back analysis for the identification of the geomechanical parameters of the Yonglang landslide in China

Xigeda formation is a type of hundredmeter-thick lacustrine sediments of being prone to triggering landslides along the trunk channel and tributaries of the upper Yangtze River in China. The Yonglang landslide located near Yonglang Town of Dechang County in Sichuan Province of China, which was a typical Xigeda formation landslide, was stabilized by anti-slide piles. Loading tests on a loading-test pile were conducted to measure the displacements and moments. The uncertainty of the tested geomechanical parameters of the Yonglang landslide over certain ranges would be problematic during the evaluation of the landslide. Thus, uniform design was introduced in the experimental design,and by which, numerical analyses of the loading-test pile were performed using Fast Lagrangian Analysis of Continua(FLAC3D) to acquire a database of the geomechanical parameters of the Yonglang landslide and the corresponding displacements of the loadingtest pile. A three-layer back-propagation neural network was established and trained with the database, and then tested and verified for its accuracy and reliability in numerical simulations. Displacement back analysis was conducted by substituting the displacements of the loading-test pile to the well-trained three-layer back-propagation neural network so as to identify the geomechanical parameters of the Yonglang landslide. The neuralnetwork-based displacement back analysis method with the proposed methodology is verified to be accurate and reliable for the identification of the uncertain geomechanical parameters of landslides.