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Stabilizing piles are extensively used as an effective landslide control treatment, and the soil arching effect is the key element for the performance of the pile system. Most previous studies on soil arching effect and its application in stabilizing piles were conducted with laboratory tests and numerical simulations, while limited efforts have been dedicated to the analytical characterization of such a soil-structure interaction. In this paper, a new stress-reduction model for soil arch in landslides is established by theoretical derivation. Our model calculation has demonstrated an exponential reduction in the stress along the direction of slipping between and behind stabilizing piles and thus justifies the observations of laboratory tests and numerical simulations. Thereafter, the analytical solutions to the two key arch shape parameters, namely the inclination angle at the foothold and the thickness of soil arch, are derived based on the proposed stress-reduction model. Then, the ultimate bearing capacity of soil arch between and behind stabilizing piles is subsequently calculated, and a three-level load sharing model for landslides is thus proposed based on the stress-reduction mode. The load sharing model can well capture the stage characteristics of the interaction between landslide mass and stabilizing piles. Finally, the calculation model of spacing between stabilizing piles is established based on the proposed stress-reduction model, and it turns to be good in field application. The findings of this study can contribute to a better understanding of the soil arching effect as well as a better design of the stabilizing piles.

Discontinuities have huge impact on civil and mining engineering. To understand the spatial features of discontinuities, it is common to group them into different sets based on orientation. In this paper, a new algorithm is introduced for the identification of discontinuity sets. The new algorithm is developed by combined fuzzy C-means algorithm with variable length string genetic algorithm. In the new method, the number of discontinuity sets is not the necessary input parameter any more. This method is robust, global optimal and totally automatic. To verify its validity, the new method was firstly applied to an artificial data as well as a published data. For artificial data set, the assignment error rate is only 7.4%. For published data set, only 2 discontinuities are assigned to wrong sets. The results indicate that the new algorithm is better than fuzzy C-means algorithm and comparable with other common methods. Afterwards, the new method was utilized to analyze the orientation data sampled at an underground storage cavern site. The new method determines that the ideal number of sets is 3. The new method provided satisfactory results, which confirm its effectiveness and convenience.

In this study, the cluster analysis (CA), probabilistic methods, and artificial neural networks (ANNs) are used to predict landslide susceptibility. The Geographic Information System (GIS) is used as the basic tool for spatial data management. CA is applied to select non-landslide dataset for later analysis. A probabilistic method is suggested to calculate the rating of the relative importance of each class belonging to each conditional factor. ANN is applied to calculate the weight (i.e., relative importance) of each factor. Using the ratings and the weights, it is proposed to calculate the landslide susceptibility index (LSI) for each pixel in the study area. The obtained LSI values can then be used to construct the landslide susceptibility map. The aforementioned proposed method was applied to the Longfeng town, a landslide-prone area in Hubei province, China. The following eight conditional factors were selected: lithology, slope angle, distance to stream/reservoir, distance to road, stream power index (SPI), altitude, curvature, and slope aspect. To assess the conditional factor effects, the weights were calculated for four cases, using 8 factors, 6 factors, 5 factors, and 4 factors, respectively. Then, the results of the landslide susceptibility analysis for these four cases, with and without weighting, were obtained. To validate the process, the receiver operating characteristics (ROC) curve and the area under the curve (AUC) were applied. In addition, the results were compared with the existing landslide locations. The validation results showed good agreement between the existing landslides and the computed susceptibility maps. The results with weighting were found to be better than that without weighting. The best accuracy was obtained for the case with 5 conditional factors with weighting.

This study validated the robust performances of the recently proposed comprehensive landslide susceptibility index model (CLSI) for landslide susceptibility mapping (LSM) by comparing it to the logistic regression (LR) and the analytical hierarchy process information value (AHPIV) model. Zhushan County in China, with 373 landslides identified, was used as the study area. Eight conditioning factors (lithology, slope structure, slope angle, altitude, distance to river, stream power index, slope length, distance to road) were acquired from digital elevation models (DEMs), field survey, remote sensing imagery, and government documentary data. Results indicate that the CLSI model has the highest accuracy and the best classification ability, although all three models can produce reasonable landslide susceptibility (LS) maps. The robust performance of the CLSI model is due to its weight determination by a back-propagation neural network (BPNN), which successfully captures the nonlinear relationship between landslide occurrence and the conditioning factors.

For deep-seated landslides, thermal pressurization in shear zone has been considered an important cause of high-speed collapse. To quantify this mechanism, this study proposed a quasi two-dimensional friction-thermo-hydro-mechanical (FTHM) model, concerning the mechanisms of material frictional softening and thermo-hydro-mechanical softening during the start-up phase of high-speed landslides. In this model, the intact slide mass was divided into a lot of small slide blocks. The dynamic equations of each block, and the heat equations, pore pressure equations of each shear band were established respectively. The model fully considered the morphological characteristics of landslides, and variables such as velocity, temperature, excess pore pressure varied along both the normal and the tangential direction of the whole shear band during the slide mass movement. The model was applied to back-analyze the Vaiont landslide and the results were compared with existing one-dimensional models. It can be concluded that the irregular spatial shape of slide mass makes a difference on mechanism of thermo-hydro-mechanical softening which promotes the collapse, and that the quasi two-dimensional model is valid.

A geostatistical back analysis is developed, which can map spatially distributed Young's modulus (E′), cohesion (c′), and internal friction angle (ϕ′) in a rock mass by fusion of the observed displacement data from the excavation of an unlined rock cavern. It is tested and validated using numerical experiments with a synthetic heterogeneous rock mass, created with field observed spatial variability. Results of the experiments show that this approach yields unbiased estimates of E′, c′, and ϕ′ fields and quantifies their uncertainty. Further, the estimated fields predict the plastic zone distribution and displacements of cavern periphery, which are nearly identical to those of the true field.

The impoundment of China’s Three Gorges Reservoir has led to the revival of many old landslides along the bank, and the annual fluctuation in water level of 30 m is likely to affect the creep deformation of these landslides. Through a field investigation of Huangniba landslide in the Three Gorges Reservoir Area, the local deformation signs and field monitoring data were analyzed. It was preliminarily considered that this landslide was currently at the creep stage and the creep rate was affected by the reservoir level variation. In order to carry out further research on the effect of reservoir level variation on the landslide movement, the finite element method was used to establish the seepage–stress coupling model of Huangniba landslide based on the seepage–stress coupling theory and taking into account the creep behavior of landslide material. The model was used to simulate the variation characteristics of the seepage field, stress field, and displacement field in the landslide during 6 years of cycles of reservoir level variation. Based on the numerical simulation results and field monitoring data, the deformation mechanism of the landslide was discussed. Finally, the sensitivity of the parameters in the creep model to the simulation results was discussed. The results show that creep deformation is the main deformation mechanism of Huangniba landslide, and it is obviously affected by the drawdown of the reservoir water level. During drawdown of the reservoir level, the pore water pressure inside the landslide decreases, resulting in an increase in effective creep stress and creep strain rate, thus accelerating creep deformation. The results of this study will provide a reference for the deformation mechanism of such creeping landslides subjecting to reservoir water level fluctuation.

This paper develops a sequential displacement data collection and back analysis approach for mapping spatially distributed Young’s modulus (E) in a rock mass during the excavation of an unlined rock cavern (URC). Results show that this approach provides an unbiased estimate of the E field and its uncertainty. It also reveals a more detailed E distribution than kriging approach, which is based on samples of E values from boreholes before excavations. Further, predicted shear strain distribution and displacement of cavern periphery based on the estimates from this approach are more accurate than those based on the kriging estimate.

A cross-correlation analysis is conducted to determine the impacts of the heterogeneity of hydraulic conductivity K s , soil cohesion c ′ and soil friction angle (tan φ ′) on the uncertainty of slope stability in time and space during rainfall. We find the relative importance of tan φ ′ and c ′ depends on the effective stress. While the sensitivity of the stability to the variability of K s is small, the large coefficient of variation of K s may exacerbate the variability of pore-water pressure. Therefore, characterizing the heterogeneity of hydraulic properties and pore-water distribution in the field is critical to the stability analysis.

This study proposes a cross-correlation map-based borehole deployment approach for two-dimensional probabilistic slope stability analysis. This approach designs the layout of the proper number of boreholes based on the cross-correlation between the factor of safety and spatially variable soil strength every part of a slope. Numerically synthesized, undrained slopes are investigated as examples to illustrate the effectiveness of the proposed approach. Results demonstrate that the proposed approach is viable, and the cross-correlation maps are the appropriate metric for design slope borehole deployment. Using the cross-correlation maps, a small number of boreholes can sufficiently capture the large-scale heterogeneities that are critical to the slope stability. This information can help to identify the slip surface and improve the slope stability analysis. The small-scale heterogeneity, due to its short correlation structure or the residual covariance of the soil property field after conditioning using the borehole data, leads to a small amount of uncertainty in slope stability analysis. This small uncertainty could be vital to the slope stability analysis when the slope stability is close to the limit equilibrium state.

This paper develops an analytical approach for reliability analysis of infinite slope stability in presence of spatially variable shear strength parameters. The analytical approach considers spatial autocorrelation of each parameter and cross-correlations between different parameters. It is robust, computational efficient and provides insight to the importance of spatial correlation scale on slope reliability analysis. This paper also explores the difference in continuous and discrete random fields and emphasizes the importance of fine discretization in relation to correlation scale. Finally, it shows that conditioning the stability analysis with information about trends and spatial data leads to reliability assessments with less uncertainty.

A displacement back analysis algorithm is developed, considering the time-dependent effect of the rock mass. It can map spatially distributed the first elastic modulus ( E 1 ), viscidity coefficient ( η 1 ), and the second elastic modulus ( E 2 ) of the Kelvin-Voigt viscoelastic constitutive model (VCM) and the Poynting-Thomson VCM in a rock mass by fusion of the observed displacement data from the excavation of an unlined rock cavern. The algorithm is tested and validated using numerical experiments with a synthetic heterogeneous rock mass. The results of the experiments show that this approach yields unbiased estimates of E 1 , η 1 , and E 2 fields and quantifies their uncertainty. Further, the estimated fields closely predict shear strain distribution and displacements field in the example.

Rock discontinuities significantly influence the deformation as well as strength of rock masses. Identification of rock discontinuity sets is one of the fundamental issue in rock mechanics. In this study, a new clustering method is developed to automatically identify rock discontinuity sets. The method is established on account of differential evolution, which is a robust and global optimization algorithm. An improved encoding approach was used to realize the full automation of algorithm. The main parameters of the algorithm are determined by self-adaptation techniques. The performance of the new algorithm was studied using an artificial data set. The clustering results demonstrate that the new algorithm could well identify discontinuity sets. Furthermore, the new algorithm is applied to analyzing discontinuity data collected at an underground cavern site, and satisfactory result is obtained. Additional advantage is that the method is totally automatic, without selecting proper initial cluster centers and specifying the number of discontinuity sets.

This study focuses on mercury slag in the Tongren area of Guizhou Province, China. Computed tomography (CT) is used with uniaxial and triaxial compression tests to examine the mechanical changes in cemented mercury slag and its formation. The CT results for the uniaxial compression test reveal the overall failure process of the mercury slag structure. Based on the coarse-grained soil triaxial test, a modified Duncan-Chang model is compared with the actual monitoring results and is found to be suitable for the analysis of the slag constitutive model.