排序方式： 共有51条查询结果，搜索用时 15 毫秒
1.
T. Roy Choudhury Kanan Datta Suman Majumdar Raghunath Ghara Aseem Paranjape Rajesh Mondal Somnath Bharadwaj Saumyadip Samui 《Journal of Astrophysics and Astronomy》2016,37(4):29
Studying the cosmic dawn and the epoch of reionization through the redshifted 21-cm line are among the major science goals of the SKA1. Their significance lies in the fact that they are closely related to the very first stars in the Universe. Interpreting the upcoming data would require detailed modelling of the relevant physical processes. In this article, we focus on the theoretical models of reionization that have been worked out by various groups working in India with the upcoming SKA in mind. These models include purely analytical and semi-numerical calculations as well as fully numerical radiative transfer simulations. The predictions of the 21-cm signal from these models would be useful in constraining the properties of the early galaxies using the SKA data. 相似文献
2.
Sarat Kumar Das Pijush Samui Shakilu Zama Khan Nagarathnam Sivakugan 《Central European Journal of Geosciences》2011,3(4):449-461
Stability with first time or reactivated landslides depends upon the residual shear strength of soil. This paper describes prediction of the residual strength of soil based on index properties using two machine learning techniques. Different Artificial Neural Network (ANN) models and Support Vector Machine (SVM) techniques have been used. SVM aims at minimizing a bound on the generalization error of a model rather than at minimizing the error on the training data only. The ANN models along with their generalizations capabilities are presented here for comparisons. This study also highlights the capability of SVM model over ANN models for the prediction of the residual strength of soil. Based on different statistical parameters, the SVM model is found to be better than the developed ANN models. A model equation has been developed for prediction of the residual strength based on the SVM for practicing geotechnical engineers. Sensitivity analyses have been also performed to investigate the effects of different index properties on the residual strength of soil. 相似文献
3.
经过15年时间我们发展出一套技术,即利用钻孔井壁的非致命性破裂,包括压性破裂、钻探诱发的张性破裂以及与切穿井孔断层的滑动有关的应力扰动观测值,来确定任意向井和钻孔中的全应力张量。这些技术已延伸应用到石油工业中,也应用到矿山开采的钻孔岩芯取样中,以取得开采区周围应力集中影响的区域内外的应力状态。条件允许时,可用水压致裂法估计最小主应力值,但不能估计最大水平主应力值。作者在文中先回顾了这套方法的概念,然后对两个复杂实例进行了研究。第1个实例涉及到圣安德烈斯断层深部观测站（San Andreas Fault Observatory at Depth,SAFOD）计划第1阶段钻探应力状态的确定,SAFOD计划是一个钻穿加州中部圣安德烈斯断层的科学钻井计划。第2个实例涉及到确定南非一个极深矿周围的地壳应力状态。这些研究表明,在相当大的深度范围内,斜井钻孔破裂观测值与应力大小和方向是一致的。 相似文献
4.
5.
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
6.
Kardani Navid Bardhan Abidhan Gupta Shubham Samui Pijush Nazem Majidreza Zhang Yanmei Zhou Annan 《Acta Geotechnica》2022,17(4):1239-1255
Acta Geotechnica - It is a problematic task to perform petro-physical property prediction of carbonate reservoir rocks in most cases, specifically for permeability prediction since a carbonate rock... 相似文献
7.
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
8.
The determination of seismic liquefaction potential of soil is an important task in geotechnical engineering. This article uses Minimax Probability Machine (MPM) for determination of seismic liquefaction potential of soil based on Standard Penetration Test value (N). MPM is developed based on the use of hyperplanes. It is a discriminant classifier. This study uses MPM as a classification tool. MPM uses the database collected from Chi–Chi earthquake. Two models (MODEL I and MODEL II) have been developed. MODEL I uses Cyclic Stress Ratio and N as input variables. Peck Ground Acceleration and N have been adopted as inputs for MODEL II. The performance of MODEL I and MODEL II are 97.67 and 96.51 % respectively. The performance of MODEL II is 94.11 % for the global data. The developed MPM shows good generalization capability. The results show that the developed MPM has ability for determination of seismic liquefaction potential of soil. 相似文献
9.
T. G. Sitharam Pijush Samui P. Anbazhagan 《Geotechnical and Geological Engineering》2008,26(5):503-517
Geospatial technology is increasing in demand for many applications in geosciences. Spatial variability of the bed/hard rock
is vital for many applications in geotechnical and earthquake engineering problems such as design of deep foundations, site
amplification, ground response studies, liquefaction, microzonation etc. In this paper, reduced level of rock at Bangalore,
India is arrived from the 652 boreholes data in the area covering 220 km2. In the context of prediction of reduced level of rock in the subsurface of Bangalore and to study the spatial variability
of the rock depth, Geostatistical model based on Ordinary Kriging technique, Artificial Neural Network (ANN) and Support Vector
Machine (SVM) models have been developed. In Ordinary Kriging, the knowledge of the semi-variogram of the reduced level of
rock from 652 points in Bangalore is used to predict the reduced level of rock at any point in the subsurface of the Bangalore,
where field measurements are not available. A new type of cross-validation analysis developed proves the robustness of the
Ordinary Kriging model. ANN model based on multi layer perceptrons (MLPs) that are trained with Levenberg–Marquardt backpropagation
algorithm has been adopted to train the model with 90% of the data available. The SVM is a novel type of learning machine
based on statistical learning theory, uses regression technique by introducing loss function has been used to predict the
reduced level of rock from a large set of data. In this study, a comparative study of three numerical models to predict reduced
level of rock has been presented and discussed. 相似文献
10.
Rahul Ray Deepak Kumar Pijush Samui Lal Bahadur Roy A.T.C.Goh Wengang Zhang 《地学前缘(英文版)》2021,12(1):375-383
This research focuses on the application of three soft computing techniques including Minimax Probability Machine Regression(MPMR),Particle Swarm Optimization based Artificial Neural Network(ANN-PSO)and Particle Swarm Optimization based Adaptive Network Fuzzy Inference System(ANFIS-PSO)to study the shallow foundation reliability based on settlement criteria.Soil is a heterogeneous medium and the involvement of its attributes for geotechnical behaviour in soil-foundation system makes the prediction of settlement of shallow a complex engineering problem.This study explores the feasibility of soft computing techniques against the deterministic approach.The settlement of shallow foundation depends on the parametersγ(unit weight),e0(void ratio)and CC(compression index).These soil parameters are taken as input variables while the settlement of shallow foundation as output.To assess the performance of models,different performance indices i.e.RMSE,VAF,R^2,Bias Factor,MAPE,LMI,U(95),RSR,NS,RPD,etc.were used.From the analysis of results,it was found that MPMR model outperformed PSO-ANFIS and PSO-ANN.Therefore,MPMR can be used as a reliable soft computing technique for non-linear problems for settlement of shallow foundations on soils. 相似文献