How do the landslide and non-landslide sampling strategies impact landslide susceptibility assessment? d A catchment-scale case study from China

The aim of this study is to investigate the impacts of the sampling strategy of landslide and non-landslide on the performance of landslide susceptibility assessment(LSA).The study area is the Feiyun catchment in Wenzhou City,Southeast China.Two types of landslides samples,combined with seven non-landslide sampling strategies,resulted in a total of 14 scenarios.The corresponding landslide susceptibility map(LSM)for each scenario was generated using the random forest model.The receiver operating characteristic(ROC)curve and statistical indicators were calculated and used to assess the impact of the dataset sampling strategy.The results showed that higher accuracies were achieved when using the landslide core as positive samples,combined with non-landslide sampling from the very low zone or buffer zone.The results reveal the influence of landslide and non-landslide sampling strategies on the accuracy of LSA,which provides a reference for subsequent researchers aiming to obtain a more reasonable LSM.

Landslide hazard susceptibility evaluation based on SBAS-InSAR technology and SSA-BP neural network algorithm:A case study of Baihetan Reservoir Area

Landslide hazard susceptibility evaluation takes on critical significance in early warning and disaster prevention and reduction.In order to solve the problems of poor effectiveness of landslide data and complex calculation of weights for multiple evaluation factors in the existing landslide susceptibility evaluation models,in this study,a method of landslide hazard susceptibility evaluation is proposed by combining SBAS-InSAR(Small Baseline Subsets-Interferometric Synthetic Aperture Radar)and SSA-BP(Sparrow Search Algorithm-Back Propagation)neural network algorithm.The SBAS-InSAR technology is adopted to identify potential landslide hazards in the study area,update the cataloging data of landslide hazards,and 11 evaluation factors are chosen for constructing the SSA-BP model for training and validation.Baihetan Reservoir area is selected as a case study for validation.As indicated by the results,the application of SBAS-InSAR technology,combined with both ascending and descending orbit data,effectively addresses the incomplete identification of landslide hazards caused by geometric distortion of single orbit SAR data(e.g.,shadow,overlay,and perspective contraction)in deep canyon areas,thereby enabling the acquisition of up-to-date landslide hazard data.Moreover,in comparison to the conventional BP(Back Propagation)algorithm,the accuracy of the model constructed by the SSA-BP algorithm exhibits a significant increase,with mean squared error and mean absolute error reduced by 0.0142 and 0.0607,respectively.Additionally,during the process of susceptibility evaluation,the SSA-BP model effectively circumvents the issue of considerable manual interventions in calculating the weight of evaluation factors.The area under the curve of this model reaches 0.909,surpassing BP(0.835),random forest(0.792),and the information value method(0.699).The risk of landslide occurrence in the Baihetan Reservoir area is positively correlated with slope,surface temperature,and deformation rate,while it is negatively correlated with fault distance and normalized difference vegetation index.Geological lithology exerts minimal influence on the occurrence of landslides,with the risk being low in forest land and high in grassland.The method proposed in this study provides a useful reference for disaster prevention and mitigation departments to perform landslide hazard susceptibility evaluations in deep canyon areas under complex geological conditions.

Multistate transition and coupled solid-liquid modeling of motion process of long-runout landslide

The recognition,repetition and prediction of the post-failure motion process of long-runout landslides are key scientific problems in the prevention and mitigation of geological disasters.In this study,a new numerical method involving LPF3D based on a multialgorithm and multiconstitutive model was proposed to simulate long-runout landslides with high precision and efficiency.The following results were obtained:(a)The motion process of landslides showed a steric effect with mobility,including gradual disintegration and spreading.The sliding mass can be divided into three states(dense,dilute and ultradilute)in the motion process,which can be solved by three dynamic regimes(friction,collision,and inertial);(b)Coupling simulation between the solid grain and liquid phases was achieved,focusing on drag force influences;(c)Different algorithms and constitutive models were employed in phase-state simulations.The volume fraction is an important indicator to distinguish different state types and solid‒liquid ratios.The flume experimental results were favorably validated against long-runout landslide case data;and(d)In this method,matched dynamic numerical modeling was developed to better capture the realistic motion process of long-runout landslides,and the advantages of continuum media and discrete media were combined to improve the computational accuracy and efficiency.This new method can reflect the realistic physical and mechanical processes in long-runout landslide motion and provide a suitable method for risk assessment and pre-failure prediction.

GIS-based prediction method of shallow landslides induced by heavy rainfall in large mountainous areas

Rainwater runoff that does not infiltrate the soil during heavy rainfall may increase slope instability. The effect of runoff is usually neglected in conventional rainfall-induced slope failure analysis to simplify the model. To analyze the effect of runoff on slope stability, this study simultaneously simulated the effects of surface runoff and rainfall infiltration on bank slopes in the Three Gorges Reservoir Area. A shallow slope failure method that can be used to analyze runoff was proposed based on the modified Green-Ampt model, the simplified Saint-Venant model, and the infinite slope model. In this model, the modified Green–Ampt model was used to estimate the rainfall infiltration capacity and the wetting front depth. The eight-flow(D8) method and the simplified Saint-Venant model were selected to estimate the distribution of runoff. By considering the wetting front depth as the slip surface depth, the factor of safety of the slope could be determined using the infinite slope stability model. A comparison of the different models reveals that runoff can escalate the instability of certain slopes, causing stable slopes to become unstable. Comparison of the unstable areas obtained from the simulation with the actual landslide sites shows that the model proposed in this study can successfully predict landslides at these sites. The slope instability assessment model proposed in this study offers an alternative approach for estimating high-risk areas in large mountainous regions.

Identification and distribution of 13003 landslides in the northwest margin of Qinghai-Tibet Plateau based on human-computer interaction remote sensing interpretation

The periphery of the Qinghai-Tibet Plateau is renowned for its susceptibility to landslides.However,the northwestern margin of this region,characterised by limited human activities and challenging transportation,remains insufficiently explored concerning landslide occurrence and dispersion.With the planning and construction of the Xinjiang-Tibet Railway,a comprehensive investigation into disastrous landslides in this area is essential for effective disaster preparedness and mitigation strategies.By using the human-computer interaction interpretation approach,the authors established a landslide database encompassing 13003 landslides,collectively spanning an area of 3351.24 km^(2)(36°N-40°N,73°E-78°E).The database incorporates diverse topographical and environmental parameters,including regional elevation,slope angle,slope aspect,distance to faults,distance to roads,distance to rivers,annual precipitation,and stratum.The statistical characteristics of number and area of landslides,landslide number density(LND),and landslide area percentage(LAP)are analyzed.The authors found that a predominant concentration of landslide origins within high slope angle regions,with the highest incidence observed in intervals characterised by average slopes of 20°to 30°,maximum slope angle above 80°,along with orientations towards the north(N),northeast(NE),and southwest(SW).Additionally,elevations above 4.5 km,distance to rivers below 1 km,rainfall between 20-30 mm and 30-40 mm emerge as particularly susceptible to landslide development.The study area’s geological composition primarily comprises Mesozoic and Upper Paleozoic outcrops.Both fault and human engineering activities have different degrees of influence on landslide development.Furthermore,the significance of the landslide database,the relationship between landslide distribution and environmental factors,and the geometric and morphological characteristics of landslides are discussed.The landslide H/L ratios in the study area are mainly concentrated between 0.4 and 0.64.It means the landslides mobility in the region is relatively low,and the authors speculate that landslides in this region more possibly triggered by earthquakes or located in meizoseismal area.

Runout prediction of potential landslides based on the multi-source data collaboration analysis on historical cases

Long runout landslides involve a massive amount of energy and can be extremely hazardous owing to their long movement distance,high mobility and strong destructive power.Numerical methods have been widely used to predict the landslide runout but a fundamental problem remained is how to determine the reliable numerical parameters.This study proposes a framework to predict the runout of potential landslides through multi-source data collaboration and numerical analysis of historical landslide events.Specifically,for the historical landslide cases,the landslide-induced seismic signal,geophysical surveys,and possible in-situ drone/phone videos(multi-source data collaboration)can validate the numerical results in terms of landslide dynamics and deposit features and help calibrate the numerical(rheological)parameters.Subsequently,the calibrated numerical parameters can be used to numerically predict the runout of potential landslides in the region with a similar geological setting to the recorded events.Application of the runout prediction approach to the 2020 Jiashanying landslide in Guizhou,China gives reasonable results in comparison to the field observations.The numerical parameters are determined from the multi-source data collaboration analysis of a historical case in the region(2019 Shuicheng landslide).The proposed framework for landslide runout prediction can be of great utility for landslide risk assessment and disaster reduction in mountainous regions worldwide.

Effectiveness of hybrid ensemble machine learning models for landslide susceptibility analysis:Evidence from Shimla district of North-west Indian Himalayan region

The Indian Himalayan region is frequently experiencing climate change-induced landslides.Thus,landslide susceptibility assessment assumes greater significance for lessening the impact of a landslide hazard.This paper makes an attempt to assess landslide susceptibility in Shimla district of the northwest Indian Himalayan region.It examined the effectiveness of random forest(RF),multilayer perceptron(MLP),sequential minimal optimization regression(SMOreg)and bagging ensemble(B-RF,BSMOreg,B-MLP)models.A landslide inventory map comprising 1052 locations of past landslide occurrences was classified into training(70%)and testing(30%)datasets.The site-specific influencing factors were selected by employing a multicollinearity test.The relationship between past landslide occurrences and influencing factors was established using the frequency ratio method.The effectiveness of machine learning models was verified through performance assessors.The landslide susceptibility maps were validated by the area under the receiver operating characteristic curves(ROC-AUC),accuracy,precision,recall and F1-score.The key performance metrics and map validation demonstrated that the BRF model(correlation coefficient:0.988,mean absolute error:0.010,root mean square error:0.058,relative absolute error:2.964,ROC-AUC:0.947,accuracy:0.778,precision:0.819,recall:0.917 and F-1 score:0.865)outperformed the single classifiers and other bagging ensemble models for landslide susceptibility.The results show that the largest area was found under the very high susceptibility zone(33.87%),followed by the low(27.30%),high(20.68%)and moderate(18.16%)susceptibility zones.The factors,namely average annual rainfall,slope,lithology,soil texture and earthquake magnitude have been identified as the influencing factors for very high landslide susceptibility.Soil texture,lineament density and elevation have been attributed to high and moderate susceptibility.Thus,the study calls for devising suitable landslide mitigation measures in the study area.Structural measures,an immediate response system,community participation and coordination among stakeholders may help lessen the detrimental impact of landslides.The findings from this study could aid decision-makers in mitigating future catastrophes and devising suitable strategies in other geographical regions with similar geological characteristics.

Dynamic simulation insights into friction weakening effect on rapid long-runout landslides:A case study of the Yigong landslide in the Tibetan Plateau,China

This study proposed a novel friction law dependent on velocity,displacement and normal stress for kinematic analysis of runout process of rapid landslides.The well-known Yigong landslide occurring in the Tibetan Plateau of China was employed as the case,and the derived dynamic friction formula was included into the numerical simulation based on Particle Flow Code.Results showed that the friction decreased quickly from 0.64(the peak)to 0.1(the stead value)during the 5s-period after the sliding initiation,which explained the behavior of rapid movement of the landslide.The monitored balls set at different sections of the mass showed similar variation characteritics regarding the velocity,namely evident increase at the initial phase of the movement,followed by a fluctuation phase and then a stopping one.The peak velocity was more than 100 m/s and most particles had low velocities at 300s after the landslide initiation.The spreading distance of the landslide was calculated at the two-dimension(profile)and three-dimension scale,respectively.Compared with the simulation result without considering friction weakening effect,our results indicated a max distance of about 10 km from the initial unstable position,which fit better with the actual situation.

烤烟新品种川烟102的选育及特征特性

烤烟新品种川烟102是以(K326×云烟85)F_(1)为母本、(NC86×Coker176)F_(1)为父本杂交,以抗TMV和黑胫病为育种目标,经系谱法选育而成的烤烟纯系。该品种田间生长整齐,长势较强,植株为筒形,叶色绿,叶形长椭圆,农艺性状较好;该品种对TMV免疫,中抗黑胫病和赤星病,田间落黄好,较易烘烤,综合经济性状显著优于主对照品种K326,优于副对照品种云烟87;烤后原烟外观质量优于主对照品种K326,与副对照品种云烟87相当;感官质量优于主对照K326和副对照云烟87,综合质量与主对照K326、副对照云烟87相当。川烟102是一个经济性状优、品质好、适应广的烤烟纯系新品种,适宜在安徽等东南烟区、四川等西南烟区种植。

Soil chronosequence derived from landslides on the upper reach of Minjiang River,western China

Soil chronosequences derived from landslides with certain time series are the great avenue to elevate our understanding on the processes of pedogenesis,nutrient dynamics,and ecosystem evolution.However,the construction of reliable soil chronosequence from historical landslides remains intricate.Here,we presented a 22,000-year soil chronosequence from multiple landslides on the upper reach of Minjiang River,western China.The variation in a variety of pedogenesis indices and soil nutrients verified the reliability of the chronosequence.The silica-alumina ratio and silica sesquioxide ratio decreased significantly with soil age.This reflected the enrichment of Al/Fe/Ti oxides but the depletion of Si oxides with the soil development.Meanwhile,the values of the Chemical Index of Weathering and the Chemical Index of Alteration increased significantly with soil age,especially from 5 to 89 years.These variations were attributed to the soil weathering,which led to the destruction of soil minerals with the rapid loss of most of cations(e.g.,K,Na,Ca,and Mg)during the soil development.The concentrations of carbon and nitrogen in topsoil increased with soil age,and the carbon accumulation rate slowed significantly from 5,500 to 22,000 years.The total phosphorus concentrations decreased with soil age,suggesting the gradual loss of soil phosphorus with soil development.The results indicate that the landslide chronosequence established on the upper reach of Minjiang River is reliable and delineates a long-term soil development process,which will provide a great platform for further improvement of biogeochemical theories and understanding sustainable vegetation restoration.

小半径曲线条件下102车钩连挂特性分析

针对102型挡肩式车钩在小半径曲线上连挂困难的问题,以HXD2机车为研究对象,基于多体动力学建立包含精细车钩的车辆耦合动力学模型,研究车钩开合角对车钩连挂的影响,分析小半径曲线条件下车钩连挂状态,并对车钩结构提出优化措施,以提高小半径曲线车钩连挂稳定性。结果表明:连挂速度、车钩开合角、曲线半径对连挂过程有显著影响,速度越快、车钩开合角越小,曲线半径越小时连挂冲击越大;通过优化车钩结构参数可减小连挂冲动,在速度5 km/h、车钩均为全开位、半径180 m曲线条件下,挡肩块与钩尾框间隙单独增大至3 mm可使最大接触力减小64%,增大至5 mm则连挂出现剧烈晃动;车钩尾部与挡肩块间隙对应车钩自由摆角增大至3.5°可使最大接触力减小59%,增大至6.5°则车钩摆角未饱和,挡肩块不提供回复力矩;二者耦合作用下,车钩尾部与挡肩块间隙在3.4°~3.6°对应挡肩块与钩尾框间隙为2~4 mm匹配效果最好。

重载机车102型钩缓装置动态模拟方法与动力学性能研究

该研究结合试验数据提出了重载机车102型车钩缓冲装置受压动态响应的模拟方法,并研究了该机车及钩缓装置在双机重联牵引万吨列车编组下的动力学性能。基于102型钩缓装置结构特性与受压稳钩机理,采用加权离散方法将缓冲器简化为具有相同迟滞特性的多个阻抗力元,并引入剪切刚度来考虑其相互之间的剪切效应,提出了能够准确模拟钩肩止挡和缓冲器偏压特性的102型钩缓装置动力学建模方法;进而借助子结构方法搭建了“主控机车+重联机车+货车车辆+虚拟货列”的双机重联牵引万吨列车动力学模型,其中四节机车、货车车辆之间采用钩缓装置子结构连接;通过试验数据对比修正了钩缓装置模型中离散阻抗力元间的剪切刚度,并验证了所建列车动力学模型的正确性;计算分析了线路条件、结构参数、机车电制力等对系统动力学响应和运行安全性的影响规律。结果表明:重载机车在小半径曲线上施加大电制力容易引起异常增大的轮轴横向力,危及行车安全;随着车钩最大自由转角的增大,车钩偏转角和机车运行安全性指标均呈逐渐增大趋势;机车二系悬挂横向刚度增大有助于提高机车自身的受压稳定性和减小车钩偏转角,但过大横向刚度又会增大轮轨横向动态作用。

Uncertainties in landslide susceptibility prediction:Influence rule of different levels of errors in landslide spatial position

The accuracy of landslide susceptibility prediction(LSP)mainly depends on the precision of the landslide spatial position.However,the spatial position error of landslide survey is inevitable,resulting in considerable uncertainties in LSP modeling.To overcome this drawback,this study explores the influence of positional errors of landslide spatial position on LSP uncertainties,and then innovatively proposes a semi-supervised machine learning model to reduce the landslide spatial position error.This paper collected 16 environmental factors and 337 landslides with accurate spatial positions taking Shangyou County of China as an example.The 30e110 m error-based multilayer perceptron(MLP)and random forest(RF)models for LSP are established by randomly offsetting the original landslide by 30,50,70,90 and 110 m.The LSP uncertainties are analyzed by the LSP accuracy and distribution characteristics.Finally,a semi-supervised model is proposed to relieve the LSP uncertainties.Results show that:(1)The LSP accuracies of error-based RF/MLP models decrease with the increase of landslide position errors,and are lower than those of original data-based models;(2)70 m error-based models can still reflect the overall distribution characteristics of landslide susceptibility indices,thus original landslides with certain position errors are acceptable for LSP;(3)Semi-supervised machine learning model can efficiently reduce the landslide position errors and thus improve the LSP accuracies.

Integrating predictive modeling techniques with geospatial data for landslide susceptibility assessment in northern Pakistan

The eastern road section of the ChinaPakistan Economic Corridor(CPEC)traverses the challenging terrain of northern Pakistan,where frequent landslides pose a significant threat to socioeconomic development and infrastructure.However,the insufficient data on landslide hazards presents a substantial challenge to practical mitigation efforts.Therefore,we conducted an extensive study to gain insight into landslide assessment along the Mansehra-Muzaffarabad-Mirpur and Mangla(MMMM)Expressway.This study involved preparing a landslide inventory,analyzing landslide causative factors,and developing landslide susceptibility models(LSMs)using published data,remote sensing interpretations,field excursions and integrated predictive techniques.We first used Pearson's correlation coefficient(PCC),variable importance factors(VIF),and information gain ratio(IGR)to evaluate multicollinearity among the selected landslide causative factors(LCFs).Then,the topographic roughness index(TRI)with VIF>5 and PCC>0.7 was considered a redundant factor and thus removed before the data modeling.Finally,we adopted multiple machine-learning methods to analyze landslide susceptibility.The results indicate that the landslide inventory contains 1,776 events,of which 674 were classified based on geometrical and lithological configurations.The IGR results show that the rainfall,lithology,PGA,drainage density,slope,and distance to fault are the most effective LCFs.The AUC values for random forest(RF)(0.901),extreme gradient boosting(XGBoost)(0.884),and K-nearest neighbor(KNN)(0.872)remained higher than evidential belief function(EBF)(0.833),weight of evidence(WoE)(0.820),and certainty factor(CF)(0.810),respectively.The RF model outperformed all other models in terms of prediction.However,these models are accurate but newer in the area;thus,susceptible zones were verified with comprehensive field investigations.The northern and central regions accounted for the high and very high susceptibility classes in the final landslide susceptibility mapping(LSM)compared to the southern areas.

Submarine Landslide Identified in DLW3102 Core of the Northern Continental Slope, South China Sea

In this paper, we take DLW3101 core obtained at the top of the canyon(no landslide area) and DLW3102 core obtained at the bottom of the canyon(landslide area) on the northern continental slope of the South China Sea as research objects. The chronostratigraphic framework of the DLW3101 core and elemental strata of the DLW3101 core and the DLW3102 core since MIS5 are established by analyzing oxygen isotope, calcium carbonate content, and X-Ray Fluorescence(XRF) scanning elements. On the basis of the information obtained by analyzing the sedimentary structure and chemical elements in the landslide deposition, we found that the DLW3102 core shows four layers of submarine landslides, and each landslide layer is characterized by high Si, K, Ti, and Fe contents, thereby indicating terrigenous clastic sources. L1(2.15–2.44 m) occurred in MIS2, which is a slump sedimentary layer with a small sliding distance and scale. L2(15.48–16.00 m) occurred in MIS5 and is a debris flow-deposited layer with a scale and sliding distance that are greater than those of L1. L3(19.00–20.90 m) occurred in MIS5; its upper part(19.00–20.00 m) is a debris flow-deposited layer, and its lower part(20.00–20.90 m) is a sliding deposition layer. The landslide scale of L3 is large. L4(22.93–24.27 m) occurred in MIS5; its upper part(22.93–23.50 m) is a turbid sedimentary layer, and its lower part(23.50–24.27m) is a slump sedimentary layer. The landslide scale of L4 is large.

Fiber optic monitoring of an anti-slide pile in a retrogressive landslide

Anti-slide piles are one of the most important reinforcement structures against landslides,and evalu-ating the working conditions is of great significance for landslide mitigation.The widely adopted analytical methods of pile internal forces include cantilever beam method and elastic foundation beam method.However,due to many assumptions involved in calculation,the analytical models cannot be fully applicable to complex site situations,e.g.landslides with multi-sliding surfaces and pile-soil interface separation as discussed herein.In view of this,the combination of distributed fiber optic sensing(DFOS)and strain-internal force conversion methods was proposed to evaluate the working conditions of an anti-sliding pile in a typical retrogressive landslide in the Three Gorges reservoir area,China.Brillouin optical time domain reflectometry(BOTDR)was utilized to monitor the strain distri-bution along the pile.Next,by analyzing the relative deformation between the pile and its adjacent inclinometer,the pile-soil interface separation was profiled.Finally,the internal forces of the anti-slide pile were derived based on the strain-internal force conversion method.According to the ratio of calculated internal forces to the design values,the working conditions of the anti-slide pile could be evaluated.The results demonstrated that the proposed method could reveal the deformation pattern of the anti-slide pile system,and can quantitatively evaluate its working conditions.

Displacement field reconstruction in landslide physical modeling by using a terrain laser scanner e Part 2:Application and large strain/displacement and water effect analysis

Deformation analysis is fundamental in geotechnical modeling.Nevertheless,there is still a lack of an effective method to obtain the deformation field under various experimental conditions.In this study,we introduce a processebased physical modeling of a pileereinforced reservoir landslide and present an improved deformation analysis involving large strains and water effects.We collect multieperiod point clouds using a terrain laser scanner and reconstruct its deformation field through a point cloud processing workflow.The results show that this method can accurately describe the landslide surface deformation at any time and area by both scalar and vector fields.The deformation fields in different profiles of the physical model and different stages of the evolutionary process provide adequate and detailed landslide information.We analyze the large strain upstream of the pile caused by the pile installation and the consequent violent deformation during the evolutionary process.Furthermore,our method effectively overcomes the challenges of identifying targets commonly encountered in geotechnical modeling where water effects are considered and targets are polluted,which facilitates the deformation analysis at the wading area in a reservoir landslide.Eventually,combining subsurface deformation as well as numerical modeling,we comprehensively analyze the kinematics and failure mechanisms of this complicated object involving landslides and pile foundations as well as water effects.This method is of great significance for any geotechnical modeling concerning large-strain analysis and water effects.

Time series prediction of reservoir bank landslide failure probability considering the spatial variability of soil properties

Historically,landslides have been the primary type of geological disaster worldwide.Generally,the stability of reservoir banks is primarily affected by rainfall and reservoir water level fluctuations.Moreover,the stability of reservoir banks changes with the long-term dynamics of external disastercausing factors.Thus,assessing the time-varying reliability of reservoir landslides remains a challenge.In this paper,a machine learning(ML)based approach is proposed to analyze the long-term reliability of reservoir bank landslides in spatially variable soils through time series prediction.This study systematically investigated the prediction performances of three ML algorithms,i.e.multilayer perceptron(MLP),convolutional neural network(CNN),and long short-term memory(LSTM).Additionally,the effects of the data quantity and data ratio on the predictive power of deep learning models are considered.The results show that all three ML models can accurately depict the changes in the time-varying failure probability of reservoir landslides.The CNN model outperforms both the MLP and LSTM models in predicting the failure probability.Furthermore,selecting the right data ratio can improve the prediction accuracy of the failure probability obtained by ML models.

Thermo-hydro-poro-mechanical responses of a reservoir-induced landslide tracked by high-resolution fiber optic sensing nerves

Thermo-poro-mechanical responses along sliding zone/surface have been extensively studied.However,it has not been recognized that the potential contribution of other crucial engineering geological interfaces beyond the slip surface to progressive failure.Here,we aim to investigate the subsurface multiphysics of reservoir landslides under two extreme hydrologic conditions(i.e.wet and dry),particularly within sliding masses.Based on ultra-weak fiber Bragg grating(UWFBG)technology,we employ specialpurpose fiber optic sensing cables that can be implanted into boreholes as“nerves of the Earth”to collect data on soil temperature,water content,pore water pressure,and strain.The Xinpu landslide in the middle reach of the Three Gorges Reservoir Area in China was selected as a case study to establish a paradigm for in situ thermo-hydro-poro-mechanical monitoring.These UWFBG-based sensing cables were vertically buried in a 31 m-deep borehole at the foot of the landslide,with a resolution of 1 m except for the pressure sensor.We reported field measurements covering the period 2021 and 2022 and produced the spatiotemporal profiles throughout the borehole.Results show that wet years are more likely to motivate landslide motions than dry years.The annual thermally active layer of the landslide has a critical depth of roughly 9 m and might move downward in warmer years.The dynamic groundwater table is located at depths of 9e15 m,where the peaked strain undergoes a periodical response of leap and withdrawal to annual hydrometeorological cycles.These interface behaviors may support the interpretation of the contribution of reservoir regulation to slope stability,allowing us to correlate them to local damage events and potential global destabilization.This paper also offers a natural framework for interpreting thermo-hydro-poro-mechanical signatures from creeping reservoir bank slopes,which may form the basis for a landslide monitoring and early warning system.

Numerical Simulation of Rainfall-induced Xianchi Reservoir Landslide in Yunyang,Chongqing,China

A calamitous landslide happened at 22:00 on September 1,2014 in the Yunyang area of Chongqing City,southwest China,enforcing the evacuation of 508 people and damaging 23 buildings.The landslide volume comprised 1.44 million m^(3) of material in the source area and 0.4 million m^(3) of shoveled material.The debris flow runout extended 400 m vertically and 1600 m horizontally.The Xianchi reservoir landslide event has been investigated as follows:(1)samples collected from the main body of landslide were carried out using GCTS ring shear apparatus;(2)the parameters of shear and pore water pressure have been measured;and(3)the post-failure characteristics of landslide have been analyzed using the numerical simulation method.The excess pore-water pressure and erosion in the motion path are considered to be the key reasons for the long-runout motion and the scale-up of landslides,such as that at Xianchi,were caused by the heavy rainfall.The aim of this paper is to acquired numerical parameters and the basic resistance model,which is beneficial to improve simulation accuracy for hazard assessment for similar to potentially dangerous hillslopes in China and elsewhere.