FLAC3D在边坡数值分析及位移监测中应用     

张颖  孙兆涛  裴仲文 《探矿工程》2018,45(3):57-61

以辽宁省岫岩县某山头边坡为例,通过槽探、钻探、大型剪切试验等多种手段,找出合理潜在滑动面。利用FLAC3D软件对该边坡进行三维数值模拟,分析应力应变关系,得出变形较大及应力集中区域,同时利用强度折减法得出边坡的安全系数,评价其稳定性。将从边坡后缘张拉裂隙上布设的位移监测点采集到的数据,与数值模拟结果进行对比分析,验证数值模拟的可靠性,为边坡抗滑设计提供依据。  相似文献   

基于离心机模型试验的甘肃江顶崖古滑坡复活机理研究     

郭长宝  张永双  刘定涛  滑帅  任三绍  李雪  金继军 《工程地质学报》2022,30(1):164-176

位于白龙江断裂带的甘肃舟曲江顶崖古滑坡规模巨大,受断裂活动、降雨入渗与河流侵蚀和人类工程活动等因素影响,多次发生复活-堵塞白龙江灾害事件,造成极大危害。为研究江顶崖古滑坡的复活机理,本文在野外地质调查的基础上,重点开展了滑体在含水率为10%、15%和20%条件下的离心机模型试验。研究表明:在滑体含水率为10%情况下,试验结束后仅在坡体中后部产生少量裂缝,但滑坡体整体还处于稳定状态; 而在滑体含水率为15%和20%情况下,滑坡均发生了破坏,在滑体含水率分别为15%、20%情况下坡体失稳所需离心加速度分别为100g和50g。试验测试分析表明,江顶崖古滑坡为推移式滑坡,其变形先从坡体中后部开始,坡体中后部产生裂缝,随后裂缝逐渐向前缘扩展,最终裂缝贯通造成滑坡滑动破坏。滑坡体的变形过程主要分为3个阶段: ①变形启动阶段(裂缝开始形成阶段); ②变形加速阶段(裂缝加速发展阶段); ③失稳阶段。通过离心模拟试验,结合野外调查分析,认为江顶崖古滑坡复活的因素主要受降雨和孔隙水压力的影响,是受前缘河流侵蚀牵引、降雨入渗造成滑坡中后部推移的耦合滑动。  相似文献   

利用分布式光纤声传感设备开展青藏高原易贡湖浅层结构探测     

张衡  徐团伟  裴顺平  赵俊猛 《地学前缘》2021,28(6):227-234

地震数据是利用地震学方法探测地下结构的基础条件,然而传统地震仪器难以获得极端环境地区(水下、高原等)的长时间、高密度连续数据。较之国际仪器厂商,国内的DAS研制相对较晚。自2016年起,国产DAS逐步应用于石油测井和城市区域地下结构探测,而运用在极端环境下的探测工作尚未见报道。中国科学院半导体研究所与青藏高原研究所经过多年合作研究,于2021年4月将自主研发的DAS系统首次应用于青藏高原的野外数据采集。本次试验同时记录了地面和水下的连续背景噪声和重锤数据。该研究利用背景噪声成像技术,获得了西藏易贡湖地区地表70 m以内的横波速度结构。本次研究为极端环境下的低成本、高密度数据采集和地下结构探测提供了理论和试验依据。  相似文献   

Landslide susceptibility modeling based on ANFIS with teaching-learning-based optimization and Satin bowerbird optimizer   总被引：1，自引：0，他引：1  

Wei Chen  Xi Chen  Jianbing Peng  Mahdi Panahi  Saro Lee 《地学前缘(英文版)》2021,12(1):93-107

As threats of landslide hazards have become gradually more severe in recent decades,studies on landslide prevention and mitigation have attracted widespread attention in relevant domains.A hot research topic has been the ability to predict landslide susceptibility,which can be used to design schemes of land exploitation and urban development in mountainous areas.In this study,the teaching-learning-based optimization(TLBO)and satin bowerbird optimizer(SBO)algorithms were applied to optimize the adaptive neuro-fuzzy inference system(ANFIS)model for landslide susceptibility mapping.In the study area,152 landslides were identified and randomly divided into two groups as training(70%)and validation(30%)dataset.Additionally,a total of fifteen landslide influencing factors were selected.The relative importance and weights of various influencing factors were determined using the step-wise weight assessment ratio analysis(SWARA)method.Finally,the comprehensive performance of the two models was validated and compared using various indexes,such as the root mean square error(RMSE),processing time,convergence,and area under receiver operating characteristic curves(AUROC).The results demonstrated that the AUROC values of the ANFIS,ANFIS-TLBO and ANFIS-SBO models with the training data were 0.808,0.785 and 0.755,respectively.In terms of the validation dataset,the ANFISSBO model exhibited a higher AUROC value of 0.781,while the AUROC value of the ANFIS-TLBO and ANFIS models were 0.749 and 0.681,respectively.Moreover,the ANFIS-SBO model showed lower RMSE values for the validation dataset,indicating that the SBO algorithm had a better optimization capability.Meanwhile,the processing time and convergence of the ANFIS-SBO model were far superior to those of the ANFIS-TLBO model.Therefore,both the ensemble models proposed in this paper can generate adequate results,and the ANFIS-SBO model is recommended as the more suitable model for landslide susceptibility assessment in the study area considered due to its excellent accuracy and efficiency.  相似文献   

Spatial landslide susceptibility assessment using machine learning techniques assisted by additional data created with generative adversarial networks     

Husam A.H.Al-Najjar  Biswajeet Pradhan 《地学前缘(英文版)》2021,12(2):625-637

In recent years,landslide susceptibility mapping has substantially improved with advances in machine learning.However,there are still challenges remain in landslide mapping due to the availability of limited inventory data.In this paper,a novel method that improves the performance of machine learning techniques is presented.The proposed method creates synthetic inventory data using Generative Adversarial Networks(GANs)for improving the prediction of landslides.In this research,landslide inventory data of 156 landslide locations were identified in Cameron Highlands,Malaysia,taken from previous projects the authors worked on.Elevation,slope,aspect,plan curvature,profile curvature,total curvature,lithology,land use and land cover(LULC),distance to the road,distance to the river,stream power index(SPI),sediment transport index(STI),terrain roughness index(TRI),topographic wetness index(TWI)and vegetation density are geo-environmental factors considered in this study based on suggestions from previous works on Cameron Highlands.To show the capability of GANs in improving landslide prediction models,this study tests the proposed GAN model with benchmark models namely Artificial Neural Network(ANN),Support Vector Machine(SVM),Decision Trees(DT),Random Forest(RF)and Bagging ensemble models with ANN and SVM models.These models were validated using the area under the receiver operating characteristic curve(AUROC).The DT,RF,SVM,ANN and Bagging ensemble could achieve the AUROC values of(0.90,0.94,0.86,0.69 and 0.82)for the training;and the AUROC of(0.76,0.81,0.85,0.72 and 0.75)for the test,subsequently.When using additional samples,the same models achieved the AUROC values of(0.92,0.94,0.88,0.75 and 0.84)for the training and(0.78,0.82,0.82,0.78 and 0.80)for the test,respectively.Using the additional samples improved the test accuracy of all the models except SVM.As a result,in data-scarce environments,this research showed that utilizing GANs to generate supplementary samples is promising because it can improve the predictive capability of common landslide prediction models.  相似文献   

Landslide susceptibility zonation method based on C5.0 decision tree and K-means cluster algorithms to improve the efficiency of risk management   总被引：1，自引：0，他引：1  

Zizheng Guo  Yu Shi  Faming Huang  Xuanmei Fan  Jinsong Huang 《地学前缘(英文版)》2021,12(6):243-261

Machine learning algorithms are an important measure with which to perform landslide susceptibility assessments,but most studies use GIS-based classification methods to conduct susceptibility zonation.This study presents a machine learning approach based on the C5.0 decision tree(DT)model and the K-means cluster algorithm to produce a regional landslide susceptibility map.Yanchang County,a typical landslide-prone area located in northwestern China,was taken as the area of interest to introduce the proposed application procedure.A landslide inventory containing 82 landslides was prepared and subse-quently randomly partitioned into two subsets:training data(70％landslide pixels)and validation data(30％landslide pixels).Fourteen landslide influencing factors were considered in the input dataset and were used to calculate the landslide occurrence probability based on the C5.0 decision tree model.Susceptibility zonation was implemented according to the cut-off values calculated by the K-means clus-ter algorithm.The validation results of the model performance analysis showed that the AUC(area under the receiver operating characteristic(ROC)curve)of the proposed model was the highest,reaching 0.88,compared with traditional models(support vector machine(SVM)=0.85,Bayesian network(BN)=0.81,frequency ratio(FR)=0.75,weight of evidence(WOE)=0.76).The landslide frequency ratio and fre-quency density of the high susceptibility zones were 6.76/km2 and 0.88/km2,respectively,which were much higher than those of the low susceptibility zones.The top 20％interval of landslide occurrence probability contained 89％of the historical landslides but only accounted for 10.3％of the total area.Our results indicate that the distribution of high susceptibility zones was more focused without contain-ing more"stable"pixels.Therefore,the obtained susceptibility map is suitable for application to landslide risk management practices.  相似文献   

Centrifuge modelling of landslides and landslide hazard mitigation: A review     

《地学前缘(英文版)》2023,14(1):101493

Landslides are serious geohazards that occur under a variety of climatic conditions and can cause many casualties and significant economic losses. Centrifuge modelling, as a representative type of physical modelling, provides a realistic simulation of the stress level in a small-scale model and has been applied over the last 50 years to develop a better understanding of landslides. With recent developments in this technology, the application of centrifuge modelling in landslide science has significantly increased. Here, we present an overview of physical models that can capture landslide processes during centrifuge modelling. This review focuses on (i) the experimental principles and considerations, (ii) landslide models subjected to various triggering factors, including centrifugal acceleration, rainfall, earthquakes, water level changes, thawing permafrost, excavation, external loading and miscellaneous conditions, and (iii) different methods for mitigating landslides modelled in centrifuge, such as the application of nails, piles, geotextiles, vegetation, etc. The behaviors of all the centrifuge models are discussed, with emphasis on the deformation and failure mechanisms and experimental techniques. Based on this review, we provide a best-practice methodology for preparing a centrifuge landslide test and propose further efforts in terms of the seven aspects of model materials, testing design and equipment, measurement methods, scaling laws, full-scale test applications, landslide early warning, and 3D modelling to better understand the complex behaviour of landslides.  相似文献   

M5'模型树算法和无人机航拍影像在震后崩塌滑坡分析中的应用——以鲁甸M_S6.5地震为例     

王伟平  杨建思  高瑜  徐志强  姜旭东  郑钰  彭朝勇 《中国地震》2017,33(1):68-80

利用高分辨率无人机航拍影像,结合基本地质资料,分析了影响2014年8月3日鲁甸M_S6.5地震震后崩塌滑坡分布的主要因素,使用M5'模型树算法建立了崩塌滑坡密度与其影响因子间的分段线性模型,并检验了该模型的预测性能。结果表明,地震诱发的崩塌滑坡分布受断层距、岩土体结构强度、坡度、植被条件等的影响,其中,断层距、岩土体结构强度及坡度等为主要影响因素;崩塌滑坡易发生在结构破裂区及坡度为38°~50°的区域,其分布密度随断层距的增加而减小;利用M5'模型树算法建立的模型体现出崩塌滑坡分布与其影响因子间复杂的非线性关系,模型检验结果显示,理论模型与实际关联函数间的相关系数达到0.88,因此,可利用该模型预测地震诱发的崩塌滑坡的分布。  相似文献   

利用近震高频接收函数研究四川理县西山村滑坡体结构   总被引：2，自引：1，他引：1       下载免费PDF全文

危自根  储日升  李志伟  盛敏汉  张海江  王宝善 《地球物理学报》2017,60(10):3793-3803

远震接收函数已广泛用于反演台站下方的结构,然而由于地球的非弹性衰减作用,远震数据较难获得高频接收函数,对浅地表结构约束不足.为了克服这一问题,我们使用近震数据的高频接收函数来研究浅表速度结构,并应用于四川理县西山村滑坡体上3个宽频带地震仪记录到的近震事件.本文发展了接收函数V_P-k(V_P/V_S)叠加方法,结合接收函数H-k叠加和波形反演方法获得了台站下方滑坡体的厚度、S波速度和平均V_P/V_S比,并与钻孔得到的滑坡体厚度进行对比.结果表明,滑坡体具有小尺度的横向不均匀性,台站下方滑坡体的平均V_P/V_S比在2.4~3.1之间变化并且在底层存在78~143m·s~(-1)左右的S波低速层.本文观测到的高V_P/V_S比和底层低的S波速度结构,与电磁法获得的滑坡体底层低的电阻率和底部富水特征一致,表明滑坡体h1底界面的抗剪强度相对较弱,是潜在的滑坡危险区域.本文研究结果表明,利用近震接收函数能有效约束浅表的速度结构,进而能为滑坡灾害治理提供一定的地震学参考.  相似文献   

APPLICATION OF UAVLS TO RAPID GEOLOGICAL SURVEYS          下载免费PDF全文

SHAO Yan-xiu  ZHANG Bo  ZOU Xiao-bo  WANG Ai-guo  ZHANG Fan-yu  YUAN Dao-yang  LIU Xing-wang  HE Wen-gui 《地震地质》2017,39(6):1185-1197

Three-dimensional scanning with LiDAR has been widely used in geological surveys. The LiDAR with high accuracy is promoting geoscience quantification. And it will be much more convenient, efficient and useful when combining it with the Unmanned Aerial Vehicle (UAV). This study focuses on UAV-based Laser Scanning (UAVLS)geological field mapping, taking two examples to present advantages of the UAVLS in contrast with other mapping methods. For its usage in active fault mapping, we scanned the Nanpo village site on the Zhangxian segment of the West Qinling north-edge fault. It effectively removed the effects of buildings and vegetation, and uncovered the fault trace. We measured vertical offset of 1.3m on the terrace T₁ at the Zhang river. Moreover, we also scanned landslide features at the geological hazard observatory of Lanzhou University in the loess area. The scanning data can help understand how micro-topography affects activation of loess landslides. The UAVLS is time saving in the field, only spending about half an hour to scan each site. The amount of average points per meter is about 600, which can offer topography data with resolution of centimeter. The results of this study show that the UAVLS is expected to become a common, efficient and economic mapping tool.  相似文献