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1.
This article examines the capability of Minimax Probability Machine (MPM) for the determination of stability of slope. MPM is constructed within a probabilistic framework. This study uses MPM as classification and regression tools. Unit weight (γ), cohesion (c), angle of internal friction (φ), slope angle (β), height (H) and pore water pressure coefficient (ru) have been used as inputs of the MPM model. The outputs of MPM are stability status of slope and factor of safety (F). The results of MPM have been compared with the artificial neural network models. The experimental results demonstrate that the developed MPM is a promising tool for the determination of stability of slope.  相似文献   

2.
This study employs two statistical learning algorithms (Support Vector Machine (SVM) and Relevance Vector Machine (RVM)) for the determination of ultimate bearing capacity (qu) of shallow foundation on cohesionless soil. SVM is firmly based on the theory of statistical learning, uses regression technique by introducing varepsilon‐insensitive loss function. RVM is based on a Bayesian formulation of a linear model with an appropriate prior that results in a sparse representation. It also gives variance of predicted data. The inputs of models are width of footing (B), depth of footing (D), footing geometry (L/B), unit weight of sand (γ) and angle of shearing resistance (?). Equations have been developed for the determination of qu of shallow foundation on cohesionless soil based on the SVM and RVM models. Sensitivity analysis has also been carried out to determine the effect of each input parameter. This study shows that the developed SVM and RVM are robust models for the prediction of qu of shallow foundation on cohesionless soil. Copyright © 2010 John Wiley & Sons, Ltd.  相似文献   

3.
Statistical learning algorithms provide a viable framework for geotechnical engineering modeling. This paper describes two statistical learning algorithms applied for site characterization modeling based on standard penetration test (SPT) data. More than 2700 field SPT values (N) have been collected from 766 boreholes spread over an area of 220 sqkm area in Bangalore. To get N corrected value (Nc), N values have been corrected (Nc) for different parameters such as overburden stress, size of borehole, type of sampler, length of connecting rod, etc. In three‐dimensional site characterization model, the function Nc=Nc (X, Y, Z), where X, Y and Z are the coordinates of a point corresponding to Nc value, is to be approximated in which Nc value at any half‐space point in Bangalore can be determined. The first algorithm uses least‐square support vector machine (LSSVM), which is related to a ridge regression type of support vector machine. The second algorithm uses relevance vector machine (RVM), which combines the strengths of kernel‐based methods and Bayesian theory to establish the relationships between a set of input vectors and a desired output. The paper also presents the comparative study between the developed LSSVM and RVM model for site characterization. Copyright © 2009 John Wiley & Sons, Ltd.  相似文献   

4.
Circular failure is generally observed in the slope of soil, highly jointed rock mass, mine dump and weak rock. Accurate estimation of the safety factor (SF) of slopes and their performance is not an easy task. In this research, based on rock engineering systems (RES), a new approach for the estimation of the SF is presented. The introduced model involves six effective parameters on SF [unit weight (γ), pore pressure ratio (r u), height (H), angle of internal friction (φ), cohesion (C) and slope angle (\(\beta\))], while retaining simplicity as well. In the case of SF prediction, all the datasets were divided randomly to training and testing datasets for proposing the RES model. For comparison purposes, nonlinear multiple regression models were also employed for estimating SF. The performances of the proposed predictive models were examined according to two performance indices, i.e., coefficient of determination (R 2) and mean square error. The obtained results of this study indicated that the RES is a reliable method to predict SF with a higher degree of accuracy in comparison with nonlinear multiple regression models.  相似文献   

5.
The residual strength of clay is very important to evaluate long term stability of proposed and existing slopes and for remedial measure for failure slopes. Various attempts have been made to correlate the residual friction angle (r) with index properties of soil. This paper presents a neural network model to predict the residual friction angle based on clay fraction and Atterberg's limits. Different sensitivity analysis was made to find out the important parameters affecting the residual friction angle. Emphasis is placed on the construction of neural interpretation diagram, based on the weights of the developed neural network model, to find out direct or inverse effect of soil properties on the residual shear angle. A prediction model equation is established with the weights of the neural network as the model parameters.  相似文献   

6.
This paper examines the potential of relevance vector machine (RVM) in prediction of ultimate capacity of driven piles in cohesionless soils. RVM is a Bayesian framework for regression and classification with analogous sparsity properties to the support vector machine (SVM). In this study, RVM has been used as a regression tool. It can be seen as a probabilistic version of SVM. In this study, RVM model outperforms the artificial neural network (ANN) model based on root-mean-square-error (RMSE) and mean-absolute-error (MAE) performance criteria. It also estimates the prediction variance. An equation has been developed for the prediction of ultimate capacity of driven piles in cohesionless soils based on the RVM model. The results show that the RVM model has the potential to be a practical tool for the prediction of ultimate capacity of driven piles in cohesionless soils.  相似文献   

7.
Probabilistic Stability Evaluation of Oppstadhornet Rock Slope,Norway   总被引：1，自引：1，他引：0  
Probabilistic analyses provide rational means to treat the uncertainties associated with underlying parameters in a systematic manner. The stability of a 734-m-high jointed rock slope in the west of Norway, the Oppstadhornet rock slope, is investigated by using a probabilistic method. The first-order reliability method (FORM) is used for probabilistic modeling of the plane failure problem in the rock slope. The Barton–Bandis (BB) shear strength criterion is used for the limit state equation. The statistical distributions of the BB criterion parameters, for which comprehensive data were collected and statistically analyzed, are determined by using distribution fitting algorithms. The sensitivity of the FORM model for the BB criterion is also investigated. It is found that the model is most sensitive to the mean value of the residual friction angle (ϕ r) and least sensitive to the mean value of the slope angle (β f). It is also found that the standard deviation of joint compressive strength (JCS) causes the greatest difference in the reliability index, which has the least sensitivity to the change in the mean and standard deviation of joint roughness coefficient (JRC).  相似文献   

8.
Assessment of tunnel stability has become increasingly crucial as more and more tunnels are built in difficult terrains such as sloping ground. The required support pressure on the tunnel walls associates both tunnel stability and liner design considerations. The present analysis attempts to find a uniform internal pressure which can support a circular tunnel built in a sloping ground with a particular level of stability in cohesive-frictional soils. The lower bound finite element limit analysis has been applied to find the required minimum uniform internal support pressure presented as a non-dimensional term p/c; where p is the minimum normal internal pressure on the tunnel boundary to avoid collapse and c is the cohesion of soil. The variation of p/c is presented for a range of normalised embedment depth of tunnel (H/D), stability number (γD/c), internal friction angle of soil (?) and slope angle (β); where H is the crown depth of the tunnel, D is the tunnel diameter and γ is the unit weight of soil. Appropriate comparisons have been carried out with available literature. Failure patterns of the tunnel have also been studied to understand the extent and the type of failure zone which may generate during the collapse.  相似文献   

9.
Two algorithms are outlined, each of which has interesting features for modeling of spatial variability of rock depth. In this paper, reduced level of rock at Bangalore, India, is arrived from the 652 boreholes data in the area covering 220 sq⋅km. Support vector machine (SVM) and relevance vector machine (RVM) have been utilized to predict the reduced level of rock in the subsurface of Bangalore and to study the spatial variability of the rock depth. The support vector machine (SVM) that is firmly based on the theory of statistical learning theory uses regression technique by introducing ε-insensitive loss function has been adopted. RVM is a probabilistic model similar to the widespread SVM, but where the training takes place in a Bayesian framework. Prediction results show the ability of learning machine to build accurate models for spatial variability of rock depth with strong predictive capabilities. The paper also highlights the capability of RVM over the SVM model.  相似文献   

10.
Summary The critical excavation depth of a jointed rock slope is an important problem in rock engineering. This paper studies the critical excavation depth for two idealized jointed rock slopes by employing a face-to-face discrete element method (DEM). The DEM is based on the discontinuity analysis which can consider anisotropic and discontinuous deformations due to joints and their orientations. It uses four lump-points at each surface of rock blocks to describe their interactions. The relationship between the critical excavation depth D s and the natural slope angle α, the joint inclination angle θ as well as the strength parameters of the joints c r ,φ r is analyzed, and the critical excavation depth obtained with this DEM and the limit equilibrium method (LEM) is compared. Furthermore, effects of joints on the failure modes are compared between DEM simulations and experimental observations. It is found that the DEM predicts a lower critical excavation depth than the LEM if the joint structures in the rock mass are not ignored.  相似文献   

11.
Rainfall infiltration poses a disastrous threat to the slope stability in many regions around the world. This paper proposes an extreme gradient boosting (XGBoost)-based stochastic analysis framework to estimate the rainfall-induced slope failure probability. An unsaturated slope under rainfall infiltration in spatially varying soils is selected in this study to investigate the influences of the spatial variability of soil properties (including effective cohesion c′, effective friction angle φ′ and saturated hydraulic conductivity ks), as well as rainfall intensity and rainfall pattern on the slope failure probability. Results show that the proposed framework in this study is capable of computing the failure probability with accuracy and high efficiency. The spatial variability of ks cannot be overlooked in the reliability analysis. Otherwise, the rainfall-induced slope failure probability will be underestimated. It is found that the rainfall intensity and rainfall pattern have significant effect on the probability of failure. Moreover, the failure probabilities under various rainfall intensities and patterns can be easily obtained with the aid of the proposed framework, which can provide timely guidance for the landslide emergency management departments.  相似文献   

12.
IAN STATHAM 《Sedimentology》1974,21(1):149-162
Rotating drum experiments on the repose angles of mixtures of glass spheres have shown that φr (angle of shear) is strongly influenced by the proportions of the mixture. It was found that φr reached a peak value for the minimum porosity mixture; where the pore spaces between the large particles were just filled with small material; which was attributed to increased dilatation on the shear plane during avalanching. The geomorphic significance of these observations, in terms of slope development, is discussed. Secondly, the results of the experiments showed that, although more constant than φi (limiting angle of repose), φr was subject to some variation. Thus φr, as measured in a rotating drum, is not a true constant and can not be exactly analogous to φ'cv (angle of internal sliding friction at constant volume) as measured in a shearbox test—as has been previously suggested. It is tentatively suggested that at least some of the variability in φr is attributable to the magnitude of the immediately preceding value of φi, in that an unusually high value of φi, favours a lower value of φr due to the greater amount of kinetic energy released on failure.  相似文献   

13.
This paper further examines the possibility of modelling landslide as a consequence of the unstable slip in a steadily creeping slope when it is subject to perturbations, such as those induced by rainfall and earthquakes. In particular, the one-state variable friction law used in the landslide analysis by Chau is extended to a two-state variable friction law. According to this state variable friction law, the shear strength (τ) along the slip surface depends on the creeping velocity (V) as well as the two state variables (θ1 and θ2), which evolve with the ongoing slip. For translational slides, a system of three coupled non-linear first-order ordinary differential equations is formulated, and a linear stability analysis is applied to study the stability in the neighbourhood of the equilibrium solution of the system. By employing the stability classification of Reyn for three-dimensional space, it is found that equilibrium state (or critical point) of a slope may change from a ‘stable spiral’ to a ‘saddle spiral with unstable plane focus’ through a transitional state called ‘converging vortex spiral’ (i.e. bifurcation occurs), as the non-linear parameters of the slip surface evolve with its environmental changes (such as those induced by rainfall or human activities). If the one-state variable friction law is used in landslide modelling, velocity strengthening (i.e. dτss/dV > 0, where τss is the steady-state shear stress) in the laboratory always implies the stability of a creeping slope containing the same slip surface under gravitational pull. This conclusion, however, does not apply if a two-state variable friction law is employed to model the sliding along the slip surface. In particular, neither the region of stable creeping slopes in the non-linear parameter space can be inferred by that of velocity strengthening, nor the unstable region by that of velocity weakening. Copyright © 1999 John Wiley & Sons, Ltd.  相似文献   

14.
The determination of liquefaction potential of soil is an imperative task in earthquake geotechnical engineering. The current research aims at proposing least square support vector machine (LSSVM) and relevance vector machine (RVM) as novel classification techniques for the determination of liquefaction potential of soil from actual standard penetration test (SPT) data. The LSSVM is a statistical learning method that has a self-contained basis of statistical learning theory and excellent learning performance. RVM is based on a Bayesian formulation. It can generalize well and provide inferences at low computational cost. Both models give probabilistic output. A comparative study has been also done between developed two models and artificial neural network model. The study shows that RVM is the best model for the prediction of liquefaction potential of soil is based on SPT data.  相似文献   

15.
The determination of ultimate capacity (Q) of driven piles in cohesionless soil is an important task in geotechnical engineering. This article adopts Multivariate Adaptive Regression Spline (MARS) for prediction Q of driven piles in cohesionless soil. MARS uses length (L), angle of shear resistance of the soil around the shaft (?shaft), angle of shear resistance of the soil at the tip of the pile (?tip), area (A), and effective vertical stress at the tip of the pile as input variables. Q is the output of MARS. The results of MARS are compared with that of the Generalized Regression Neural Network model. An equation has been also presented based on the developed MARS. The results show the strong potential of MARS to be applied to geotechnical engineering as a regression tool. Copyright © 2011 John Wiley & Sons, Ltd.  相似文献   

16.
This paper examines the potential of least‐square support vector machine (LSVVM) in the prediction of settlement of shallow foundation on cohesionless soil. In LSSVM, Vapnik's ε‐insensitive loss function has been replaced by a cost function that corresponds to a form of ridge regression. The LSSVM involves equality instead of inequality constraints and works with a least‐squares cost function. The five input variables used for the LSSVM for the prediction of settlement are footing width (B), footing length (L), footing net applied pressure (P), average standard penetration test value (N) and footing embedment depth (d). Comparison between LSSVM and some of the traditional interpretation methods are also presented. LSSVM has been used to compute error bar. The results presented in this paper clearly highlight that the LSSVM is a robust tool for prediction of settlement of shallow foundation on cohesionless soil. Copyright © 2008 John Wiley & Sons, Ltd.  相似文献   

17.
The shear strength of rock joints in theory and practice   总被引：62，自引：10，他引：62  
SummaryThe Shear Strength of Rock Joints in Theory and Practice The paper describes an empirical law of friction for rock joints which can be used both for extrapolating and predicting shear strength data. The equation is based on three index parameters; the joint roughness coefficientJRC, the joint wall compressive strengthJCS, and the residual friction angle r . All these index values can be measured in the laboratory. They can also be measured in the field. Index tests and subsequent shear box tests on more than 100 joint samples have demonstrated that r can be estimated to within ± 1° for any one of the eight rock types investigated. The mean value of the peak shear strength angle (arctan/ n ) for the same 100 joints was estimated to within 1/2°. The exceptionally close prediction of peak strength is made possible by performing self-weight (low stress) sliding tests on blocks with throughgoing joints. The total friction angle (arctan/ n ) at which sliding occurs provides an estimate of the joint roughness coefficientJRC. The latter is constant over a range of effective normal stress of at least four orders of magnitude. However, it is found that bothJRC andJCS reduce with increasing joint length. Increasing the length of joint therefore reduces not only the peak shear strength, but also the peak dilation angle and the peak shear stiffness. These important scale effects can be predicted at a fraction of the cost of performing large scale in situ direct shear tests.With 20 Figures  相似文献   

18.
This paper reports the field evidence and the kinematical study of the motion of two blocks (A and B) mobilised by a rockfall in Lavone (Valtrompia, northern Italy) on 14 February 1987. The two sequences of impact marks left by the blocks on the ground surface were measured and the lithostratigraphical features of the debris slope were surveyed. On the basis of the field-collected input data, several computer simulations were carried out to calculate the coefficients of restitution (E) satisfying the trajectory conditions. The computed output values show that rebound trajectories require high coefficients of restitution (0.8 ≤ E ≤ 0.9). Back-calculated impact velocities range from 9.2 to 19.8 m/s. Trajectory heights vary from 0 to 2.4 m above the slope surface. Block trajectories differ considerably according to the circumstances of initial air projection, i.e. to initial rebound angle (αr). The calculated values of (αr) denote a considerable range (36°), emphasising the random nature of this parameter. The described case-history shows that rockfall computer analyses can be an effective tool to describe the bouncing propagation of single blocks but care must be taken in choosing the restitution coefficient E and the geometrical parameters of initial air projections.  相似文献   

19.
A backpropagation artificial neural network (ANN) model is developed to predict the secant friction angle of residual and fully softened soils, using data reported by Stark et al. (J Geotech Geoenviron Eng ASCE 131:575–588, 2005). In the ANN model, index properties such as liquid limit, plastic limit, activity, clay fraction and effective normal stress are used as input variables while secant residual friction angle is used as output variable. The model is verified using data that were not used for model training and testing. The results also indicate that the secant residual friction angle of cohesive soils can be predicted quite accurately using liquid limit, clay fraction and effective normal stress as input variables with R 2 = 0.93. The sensitivity analysis results indicate that plastic limit and activity have no appreciable effect on ANN predicted secant friction angles. The secant friction angle predictions of the ANN model were also compared with those of Stark’s et al. (2005) curves and the empirical formulas suggested for the same data sets by Wright (Evaluation of soil shear strengths for slope and retaining wall stability with emphasis on high plasticity clays, 2005). The comparison shows that the ANN model predictions are very close to those suggested by the Stark et al. (2005) curves but much better than the prediction of Wright’s (2005) empirical equations. The results also show that ANN is an alternative powerful tool to predict the secant friction angle of soils.  相似文献   

20.
Rock slope instabilities are a major hazard for human activities often causing economic losses, property damages and maintenance costs, as well as injuries or fatalities. For slope stability analysis of open pit mines, series of studies must be carried out in order to identify the criteria which should take into consideration. In this research geotechnical parameters; Geological Strength Index (GSI), Rock Quality Designation (RQD), Cohesion (C), angle of internal friction (φ), uniaxial compressive strength (UCS) and Rock mass deformation modulus (Em) which are obtained from data measured within geotechnical boreholes and pore pressure (U) are considered as the criteria to evaluate stability of pit No.1 of the Gole Gohar iron mine, located in Kerman province, south east of Iran. Since human judgments and preferences are often vague and complex and decision makers cannot estimate their preferences with an exact scale, we can only give linguistic assessments instead of exact ones. So fuzzy set theory introduced into Analytical Hierarchy Process (AHP). Fuzzy AHP (FAHP) is put forward to solve such uncertain problems. In this paper, FAHP method is used to determine the weights of the criteria by decision makers and then classification of the stability of blocks are determined by TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) method by the shortest distance to positive ideal solution (PIS) and the longest distance to negative ideal solution (NIS).  相似文献   

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