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21.
Sanhita Majumdar Pijush Kanti Mandal Sudip Kumar Das Rabindranath Majumdar 《洁净——土壤、空气、水》2013,41(3):291-297
Carbon bearing materials derived from rice husk (RH) have long been recognized as sorbents for diverse chemicals – both organic and inorganic. This work presents an overview of studies demonstrating a single step process of carbonization of chemically pretreated RH to produce metal impregnated silica‐carbon char designated as silicarbon materials that can be utilized in sorbing out water‐borne organic and inorganic hazardous substances (such as phenol, hexavalent chromium, fluoride, and arsenic) and air‐borne volatile organic chemicals (such as acetone, chloroform, benzene, and pyridine). The metal‐impregnated silicarbon solids derived from RH appear to constitute renewable, low‐cost, user‐friendly, and efficient materials for control systems for indoor air contamination and for industrial as well as non‐industrial hazardous aqueous pollution. 相似文献
22.
Pijush Samui 《国际地质力学数值与分析法杂志》2012,36(11):1434-1439
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
23.
Application of Relevance Vector Machine for Prediction of Ultimate Capacity of Driven Piles in Cohesionless Soils 总被引：1，自引：1，他引：0
Pijush Samui 《Geotechnical and Geological Engineering》2012,30(5):1261-1270
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
24.
Pijush Samui Tim Lansivaara Madhav R. Bhatt 《Geotechnical and Geological Engineering》2013,31(4):1329-1334
This paper investigates the feasibility of Least square support vector machine (LSSVM) model to cope the problem of implicit performance function during first order second moment (FOSM) method based slope reliability analysis. LSSVM is firmly based on the theory of statistical learning. In LSSVM, Vapnik’s ε -insensitive loss function has been replaced by a cost function which corresponds to a form of ridge regression. Here, LSSVM has been used as a regression technique to approximate implicit performance functions. A slope example has been presented for illustrating the applicability of LSSVM based FOSM method. The developed LSSVM based FOSM has been compared with the artificial neural network (ANN) and least square method. The result shows that the approximation of LSSVM can be used in the FOSM method for slope reliability analysis. 相似文献
25.
Application of support vector machine and relevance vector machine to determine evaporative losses in reservoirs 总被引：1，自引：0，他引：1
This article employs Support Vector Machine (SVM) and Relevance Vector Machine (RVM) for prediction of Evaporation Losses (E) in reservoirs. 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 based on a Bayesian formulation of a linear model with an appropriate prior that results in a sparse representation. The input of SVM and RVM models are mean air temperature (T) ( °C), average wind speed (WS) (m/sec), sunshine hours (SH)(hrs/day), and mean relative humidity (RH) (%). Equations have been also developed for prediction of E. The developed RVM model gives variance of the predicted E. A comparative study has also been presented between SVM, RVM and ANN models. The results indicate that the developed SVM and RVM can be used as a practical tool for prediction of E. Copyright © 2011 John Wiley & Sons, Ltd. 相似文献
26.
27.
Forecasting monthly precipitation using sequential modelling 总被引：1，自引：1，他引：0
In the hydrological cycle, rainfall is a major component and plays a vital role in planning and managing water resources. In this study, new generation deep learning models, recurrent neural network (RNN) and long short-term memory (LSTM), were applied for forecasting monthly rainfall, using long sequential raw data for time series analysis. “All-India” monthly average precipitation data for the period 1871–2016 were taken to build the models and they were tested on different homogeneous regions of India to check their robustness. From the results, it is evident that both the trained models (RNN and LSTM) performed well for different homogeneous regions of India based on the raw data. The study shows that a deep learning network can be applied successfully for time series analysis in the field of hydrology and allied fields to mitigate the risks of climatic extremes. 相似文献
28.
The curvature-free (k=0) FRW expanding cosmological model is developed corresponding to interacting viscous fluids and zero-mass scalar fields. In the absence of non-static scalar fields the model exhibits the existence of the initial singularity (Q=0). However, with non-negative coefficient of shear viscosity, in the presence of non-static scalar fields we find thatQ has a minimum value (0). If this epoch is treated as the initial one, it may be said that the presence of scalar fields avoids the initial singularity. Other physical behaviour that the model exhibit has been discussed. 相似文献
29.
The presence of hard and massive sandstone above the coal seam in underground coal mines often leads to delay in caving of overlying rock beds thereby causing excessive load on supports and posing danger to underground workings. The problem is more prominent in blasting gallery (BG) as well as longwall mining methods in Indian coal mines. Induced caving by blasting is a promising means for hard roof management in underground coal mines. Based on extensive studies and data collected from different mines in India, a Blastability Index (BI) has been developed which can be used for the classification of roof according to the degree of ease in caving by induced blasting. Different charge factors have also been suggested based on the Blastability Index. Due to wide change in the method of extractions, ??Cavability Index?? for longwall panel was found ineffective in case of BG method of working as well as bord and pillar working. For this reason, this proposed Blastability Index would be of immense help for caving of hard roof by induced blasting. 相似文献
30.
Application of Artificial Intelligence to Maximum Dry Density and Unconfined Compressive Strength of Cement Stabilized Soil 总被引：2，自引：1，他引：1
Sarat Kumar Das Pijush Samui Akshaya K. Sabat 《Geotechnical and Geological Engineering》2011,29(3):329-342
This paper describes two artificial intelligence techniques for prediction of maximum dry density (MDD) and unconfined compressive
strength (UCS) of cement stabilized soil. The first technique uses various artificial neural network (ANN) models such as
Bayesian regularization method (BRNN), Levenberg- Marquardt algorithm (LMNN) and differential evolution algorithm (DENN).
The second technique uses 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. The inputs of both models are liquid
limit (LL), plasticity index (PI), clay fraction (CF)%, sand (S)%, gravel Gr (%), moisture content (MC) and cement content
(Ce). The sensitivity analyses of the input parameters have been also done for both models. Based on different statistical
criteria the SVM models are found to be better than ANN models for the prediction of MDD and UCS of cement stabilized soil. 相似文献