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31.
A viscous fluid cosmological model in presence of magnetic field and zero-mass scalar fields is developed. The non-negativity condition of viscous fluid pressure prescribes a certain minimum value oft vis-a-vis of the scale factorQ(t) and at this epoch the model is found to be singularity free. 相似文献
32.
Least square support vector machine and relevance vector machine for evaluating seismic liquefaction potential using SPT 总被引：4，自引：1，他引：3
Pijush Samui 《Natural Hazards》2011,59(2):811-822
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
33.
Pijush Samui 《国际地质力学数值与分析法杂志》2012,36(1):100-110
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
34.
This paper proposes to use least square support vector machine (LSSVM) and relevance vector machine (RVM) for prediction of the magnitude (M) of induced earthquakes based on reservoir parameters. Comprehensive parameter (E) and maximum reservoir depth (H) are used as input variables of the LSSVM and RVM. The output of the LSSVM and RVM is M. Equations have been presented based on the developed LSSVM and RVM. The developed RVM also gives variance of the predicted M. A comparative study has been carried out between the developed LSSVM, RVM, artificial neural network (ANN), and linear regression models. Finally, the results demonstrate the effectiveness and efficiency of the LSSVM and RVM models. 相似文献
35.
This article adopts Multivariate Adaptive Regression Spline (MARS) for prediction of Angle of Shearing Resistance(?) of soil. MARS is an adaptive, non-parametric regression approach. Percentages of fine-grained (FG), coarse-grained (CG), liquid limit (LL), and bulk density (BD) have been used as input variables of MARS. The developed MARS gives an equation for prediction of ? of soil. The results of MARS have been compared with Genetic Expression Programming (GEP), Artificial Neural Network (ANN), and Adaptive Neuro Fuzzy Inference System (ANFIS) models. These results demonstrate that the developed MARS can be used as a robust model for determination of ? of soil. 相似文献
36.
Acta Geotechnica - Rockburst is a major instability issue faced by underground excavation projects, which is induced by the instantaneous release of a large amount of strain energy stored in rock... 相似文献
37.
Slope stability analysis: a support vector machine approach 总被引：5，自引：0，他引：5
Pijush Samui 《Environmental Geology》2008,56(2):255-267
Artificial Neural Network (ANN) such as backpropagation learning algorithm has been successfully used in slope stability problem.
However, generalization ability of conventional ANN has some limitations. For this reason, Support Vector Machine (SVM) which
is firmly based on the theory of statistical learning has been used in slope stability problem. An interesting property of
this approach is that it is an approximate implementation of a structural risk minimization (SRM) induction principle that
aims at minimizing a bound on the generalization error of a model, rather than minimizing only the mean square error over
the data set. In this study, SVM predicts the factor of safety that has been modeled as a regression problem and stability
status that has been modeled as a classification problem. For factor of safety prediction, SVM model gives better result than
previously published result of ANN model. In case of stability status, SVM gives an accuracy of 85.71%. 相似文献
38.
Kumar Deepak Samui Pijush Kim Dookie Singh Anshuman 《Geotechnical and Geological Engineering》2021,39(2):1049-1058
In this research, deep learning (DL) model is proposed to classify the soil reliability for liquefaction. The applicability of the DL model is tested in comparison with emotional backpropagation neural network (EmBP). The database encompassing cone penetration test of Chi–Chi earthquake. This study uses cone resistance (qc) and peck ground acceleration as inputs for prediction of liquefaction susceptibility of soil. The performance of developed models has been assessed by using various parameters (receiver operating characteristic, sensitivity, specificity, Phi correlation coefficient, Precision–Recall F measure). The performance of DL is excellent. Consistent results obtained from the proposed deep learning model, compared to the EmBP, indicate the robustness of the methodology used in this study. In addition, both the developed model was also tested on global earthquake data. During validation on global data, both the models shows good results based on fitness parameters. The developed classification models a simple, but also efficient decision-making tool in engineering design to quantitatively assess the liquefaction potential. The finding of this paper can be further used to capture the relationship between soil and earthquake parameters.
相似文献39.
40.
Sarat Kumar Das Pijush Samui Akshaya Kumar Sabat T. G. Sitharam 《Environmental Earth Sciences》2010,61(2):393-403
The swelling pressure of soil depends upon various soil parameters such as mineralogy, clay content, Atterberg’s limits, dry
density, moisture content, initial degree of saturation, etc. along with structural and environmental factors. It is very
difficult to model and analyze swelling pressure effectively taking all the above aspects into consideration. Various statistical/empirical
methods have been attempted to predict the swelling pressure based on index properties of soil. In this paper, the computational
intelligence techniques artificial neural network and support vector machine have been used to develop models based on the
set of available experimental results to predict swelling pressure from the inputs; natural moisture content, dry density,
liquid limit, plasticity index, and clay fraction. The generalization of the model to new set of data other than the training
set of data is discussed which is required for successful application of a model. A detailed study of the relative performance
of the computational intelligence techniques has been carried out based on different statistical performance criteria. 相似文献