三维滑坡推力计算方法探讨

常规二维滑坡推力计算没有考虑其横向变化,导致设计过于保守,三维推力计算方法的提出则能体现滑坡推力的空间分布,解决二维存在的问题.本文在三维极限平衡法统一模型的基础上,建立了求解三维滑坡推力的统一公式.利用实例,通过二维推力计算与三维计算结果的对比分析,获得三维推力的横向分布函数,有效反映出三维滑坡推力的空间分布状态,所...     

四川地震灾区三兴庙滑坡稳定性评价及治理研究

地震震裂山体是四川"5·12"地震灾区发育的具有"裂"而未"滑","松"而未"动"特征的一类典型滑坡地质灾害。通过对绵竹市清平乡三兴庙滑坡区的地形地貌和地质构造的详细描述,着重分析了地震震裂山体——三兴庙滑坡变形的破坏机制和影响因素。综合运用参数反演和室内试验等方法确定了滑体和滑带土的物理力学参数,采用滑坡推力法建立了滑坡稳定性分析模型,对5个潜在滑动带进行了不同工况计算,提出了三兴庙滑坡的综合治理方案:结合地表排水工程,在滑坡变形区主滑段前缘设置抗滑桩,通过抗滑桩将滑坡下滑力传递至深部稳定地层,并满足上部滑体不产生越顶现象;在已滑塌区后缘设置板桩式挡墙,清除危岩,平整坡面,一方面起到保护坡脚安置区的作用,另一方面可以将滑塌物质固定在上部,起到回填压坡脚之功效,促使滑体逐渐发展为自然稳定状态。     

SWAN predictions of waves observed in shallow water onshore of complex bathymetry

SWAN model predictions, initialized with directional wave buoy observations in 550-m water depth offshore of a steep, submarine canyon, are compared with wave observations in 5.0-, 2.5-, and 1.0-m water depths. Although the model assumptions include small bottom slopes, the alongshore variations of the nearshore wave field caused by refraction over the steep canyon are predicted well over the 50 days of observations. For example, in 2.5-m water depth, the observed and predicted wave heights vary by up to a factor of 4 over about 1000 m alongshore, and wave directions vary by up to about 10°, sometimes changing from south to north of shore normal. Root-mean-square errors of the predicted wave heights, mean directions, periods, and radiation stresses (less than 0.13 m, 5°, 1 s, and 0.05 m³/s² respectively) are similar near and far from the canyon. Squared correlations between the observed and predicted wave heights usually are greater than 0.8 in all water depths. However, the correlations for mean directions and radiation stresses decrease with decreasing water depth as waves refract and become normally incident. Although mean wave properties observed in shallow water are predicted accurately, nonlinear energy transfers from near-resonant triads are not modeled well, and the observed and predicted wave energy spectra can differ significantly at frequencies greater than the spectral peak, especially for narrow-band swell.     

Monitoring and warning of landslides and debris flows using an optical fiber sensor technology

Landslides and debris flows are typical geo-hazards which occur in hilly or mountainous regions. Debris flows may result from landslides. Geotechnical instrumentation plays an important role in monitoring and warning of landslides and resulted debris flows. Traditional technologies for monitoring landslides and debris flows have certain limitations. The new optical fiber sensors presented in this paper can overcome those limitations. This paper presents two new optical fiber sensor systems: one is the Fiber Bragg Grating (FBG)-based in-place inclinometer for monitoring landslides and the other is the FBG-based column-net system for monitoring debris flows. This paper presents the calibration results of FBG-based in-place inclinometers in laboratory. It is found that the calibration results are in good agreement with theoretical results. Both the FBG-based in-place inclinometers and the FBG-based column-net system have been installed at a site in Weijiagou valley, Beichuan County, Sichuan Province of China. Some preliminary results have been obtained and reported in the paper. The advantages of the FBG monitoring systems and their potential applications are also presented.     

Martian landslides in Valles Marineris: Wet or dry?

The objective of this paper is to determine whether martian landslides in Valles Marineris were wet or dry and place constraints on the availability of liquid water in Valles Marineris during the Amazonian, when the landslides occurred. We, thus, statistically compare the power-law relationship between the volume and runout distance of landslides on Earth with those in Valles Marineris, Mars. The exponent of the power-law for martian landslides is similar to that for dry landslides and volcanic flows on Earth, and differs significantly from wet debris flows on Earth. The constant of proportionality in the observed power-law relationship for martian flows is linearly proportional to gravity, as predicted from physical modeling of dry flows in which the dissipation occurs in a layer of uniform thickness. Conversion of gravitational potential energy to heat is insufficient to generate more than a few weight percent of liquid water in the landslide. We thus conclude that water did not significantly influence the dynamics of landslides in Valles Marineris. This implies predominantly dry conditions in Valles Marineris during the Amazonian.     

National-scale data-driven rainfall induced landslide susceptibility mapping for China by accounting for incomplete landslide data

China is one of the countries where landslides caused the most fatalities in the last decades.The threat that landslide disasters pose to people might even be greater in the future,due to climate change and the increasing urbanization of mountainous areas.A reliable national-scale rainfall induced landslide suscep-tibility model is therefore of great relevance in order to identify regions more and less prone to landslid-ing as well as to develop suitable risk mitigating strategies.However,relying on imperfect landslide data is inevitable when modelling landslide susceptibility for such a large research area.The purpose of this study is to investigate the influence of incomplete landslide data on national scale statistical landslide susceptibility modeling for China.In this context,it is aimed to explore the benefit of mixed effects mod-elling to counterbalance associated bias propagations.Six influencing factors including lithology,slope,soil moisture index,mean annual precipitation,land use and geological environment regions were selected based on an initial exploratory data analysis.Three sets of influencing variables were designed to represent different solutions to deal with spatially incomplete landslide information:Set 1(disregards the presence of incomplete landslide information),Set 2(excludes factors related to the incompleteness of landslide data),Set 3(accounts for factors related to the incompleteness via random effects).The vari-able sets were then introduced in a generalized additive model(GAM:Set 1 and Set 2)and a generalized additive mixed effect model(GAMM:Set 3)to establish three national-scale statistical landslide suscep-tibility models:models 1,2 and 3.The models were evaluated using the area under the receiver operating characteristics curve(AUROC)given by spatially explicit and non-spatial cross-validation.The spatial pre-diction pattern produced by the models were also investigated.The results show that the landslide inven-tory incompleteness had a substantial impact on the outcomes of the statistical landslide susceptibility models.The cross-validation results provided evidence that the three established models performed well to predict model-independent landslide information with median AUROCs ranging from 0.8 to 0.9.However,although Model 1 reached the highest AUROCs within non-spatial cross-validation(median of 0.9),it was not associated with the most plausible representation of landslide susceptibility.The Model 1 modelling results were inconsistent with geomorphological process knowledge and reflected a large extent the underlying data bias.The Model 2 susceptibility maps provided a less biased picture of landslide susceptibility.However,a lower predicted likelihood of landslide occurrence still existed in areas known to be underrepresented in terms of landslide data(e.g.,the Kuenlun Mountains in the northern Tibetan Plateau).The non-linear mixed-effects model(Model 3)reduced the impact of these biases best by introducing bias-describing variables as random effects.Among the three models,Model 3 was selected as the best national-scale susceptibility model for China as it produced the most plausible portray of rainfall induced landslide susceptibility and the highest spatially explicit predictive perfor-mance(median AUROC of spatial cross validation 0.84)compared to the other two models(median AUROCs of 0.81 and 0.79,respectively).We conclude that ignoring landslide inventory-based incomplete-ness can entail misleading modelling results and that the application of non-linear mixed-effect models can reduce the propagation of such biases into the final results for very large areas.     

Identification of landslide susceptibility zones in Gish River basin,West Bengal,India

The occurrence of landslide in the hilly region of Darjeeling during monsoon season is a matter of serious concern. Every year this natural hazard damages the major roads at several places and thus disrupts the transport and communication system in this region. This paper tries to prepare a landslide susceptibility zone (LSZ) map for the Gish River basin. A total number of 16 spatial parameters have been taken for this study and these are categorised under six factor clusters or groups for example, triggering factors, protective factor, lithological factors, morphometric factors, hydrological factors and anthropogenic factors. The LSZ map is prepared by integrating all the parameters adopting the weighting base as logistic regression. The landslide susceptibility map shows that nearly 9.11% of the area falls under the very high landslide-susceptible zone while 40.28% of the area of the total basin lies under the very low landslide-susceptible zone. The landslide-susceptible model is validated through the receiver operating characteristic curve. This curve shows 86% success rate in defining landslide-susceptible zones and 83.40% prediction rate for the occurrence of landslides. The spatial relationship between the landslide susceptibility model and other factors’ groups shows that the morphometric factors’ cluster (mainly slope) is the focalone for the determination of landslide-susceptible zone.     

Early warning system for shallow landslides using rainfall threshold and slope stability analysis

A combined cluster and regression analysis were performed for the first time to identify rainfall threshold that triggers landslide events in Amboori, Kerala, India. Amboori is a tropical area that is highly vulnerable to landslides. The 2, 3, and 5-day antecedent rainfall data versus daily rainfall was clustered to identify a cluster of critical events that could potentially trigger landslides. Further, the cluster of critical events was utilized for regression analysis to develop the threshold equations. The 5-day antecedent (x-variable) vs. daily rainfall (y-variable) provided the best fit to the data with a threshold equation of y = 80.7–0.1981x. The intercept of the equation indicates that if the 5-day antecedent rainfall is zero, the minimum daily rainfall needed to trigger the landslide in the Amboori region would be 80.7 mm. The negative coefficient of the antecedent rainfall indicates that when the cumulative antecedent rainfall increases, the amount of daily rainfall required to trigger monsoon landslide decreases. The coefficient value indicates that the contribution of the 5-day antecedent rainfall is ∼20% to the landslide trigger threshold. The slope stability analysis carried out for the area, using Probabilistic Infinite Slope Analysis Model (PISA-m), was utilized to identify the areas vulnerable to landslide in the region. The locations in the area where past landslides have occurred demonstrate lower Factors of Safety (FS) in the slope stability analysis. Thus, rainfall threshold analysis together with the FS values from slope stability can be suitable for developing a simple, cost-effective, and comprehensive early-warning system for shallow landslides in Amboori and similar regions.     

Landslide susceptibility mapping using random forest and boosted tree models in Pyeong-Chang,Korea

Landslides susceptibility maps were constructed in the Pyeong-Chang area, Korea, using the Random Forest and Boosted Tree models. Landslide locations were randomly selected in a 50/50 ratio for training and validation of the models. Seventeen landslide-related factors were extracted and constructed in a spatial database. The relationships between the observed landslide locations and these factors were identified by using the two models. The models were used to generate a landslide susceptibility map and the importance of the factors was calculated. Finally, the landslide susceptibility maps were validated. Finally, landslide susceptibility maps were generated. For the Random Forest model, the validation accuracy in regression and classification algorithms showed 79.34 and 79.18%, respectively, and for the Boosted Tree model, these were 84.87 and 85.98%, respectively. The two models showed satisfactory accuracies, and the Boosted Tree model showed better results than the Random Forest model.     

Landslide scar/soil erodibility mapping using Landsat TM/ETM+ bands 7 and 3 Normalised Difference Index: A case study of central region of Kenya

Landsat series multispectral remote sensing imagery has gained increasing attention in providing solutions to environmental problems such as land degradation which exacerbate soil erosion and landslide disasters in the case of rainfall events. Multispectral data has facilitated the mapping of soils, land-cover and structural geology, all of which are factors affecting landslide occurrence. The main aim of this research was to develop a methodology to visualize and map past landslides as well as identify land degradation effects through soil erosion and land-use using remote sensing techniques in the central region of Kenya. The study area has rugged terrain and rainfall has been the main source of landslide trigger. The methodology comprised visualizing landslide scars using a False Colour Composite (FCC) and mapping soil erodibility using FCC components applying expert based classification. The components of the FCC were: the first independent component (IC1), Principal Component (PC) with most geological information, and a Normalised Difference Index (NDI) involving Landsat TM/ETM+ band 7 and 3.The FCC components formed the inputs for knowledge-based classification with the following 13 classes: runoff, extreme erosions, other erosions, landslide areas, highly erodible, stable, exposed volcanic rocks, agriculture, green forest, new forest regrowth areas, clear, turbid and salty water. Validation of the mapped landslide areas with field GPS locations of landslide affected areas showed that 66% of the points coincided well with landslide areas mapped in the year 2000. The classification maps showed landslide areas on the steep ridge faces, other erosions in agricultural areas, highly erodible zones being already weathered rocks, while runoff were mainly fluvial deposits. Thus, landuse and rainfall processes play a major role in inducing landslides in the study area.