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Design of a piezovibrocone and calibration chamber
Samui, Pijush,Sitharam, T.G. Techno-Press 2010 Geomechanics & engineering Vol.2 No.3
This paper presents the details of indigenous development of the piezovibrocone and calibration chamber. The developed cone has a cylindrical friction sleeve of $150cm^2$ surface area, capped with a $60^{\circ}$ apex angle conical tip of $15cm^2$ cross sectional area. It has a hydraulic shaker, coupled to the cone penetrometer with a linear displacement unit. The hydraulic shaker can produce cyclic load in different types of wave forms (sine, Hover sine, triangular, rectangular and external wave) at a range of frequency 1-10 Hz with maximum amplitude of 10 cm. The piezovibrocone can be driven at the standard rate of 2 cm/sec using a loading unit of 10 ton capacity. The calibration chamber is of size $2m{\times}2m{\times}2m$. The sides of the chamber and the top as well as the bottom portions are rigid. It has a provision to apply confining pressure (to a maximum value of $4kg/cm^2$) through the flexible rubber membrane inlined with the side walls of the calibration chamber. The preliminary static as well as dynamic cone penetration tests have been done sand in the calibration chamber. From the experimental results, an attempt has been made to classify the soil based on friction ratio ($f_R$) and the cone tip resistance ($q_c$).
Modelling of reservoir-induced earthquakes: a multivariate adaptive regression spline
Samui, Pijush,Kim, Dookie Institute of Physics 2012 JOURNAL OF GEOPHYSICS AND ENGINEERING - Vol.9 No.5
<P>This paper employs a multivariate adaptive regression spline (MARS) for the prediction of the maximum magnitude (M) of reservoir-induced earthquakes based on reservoir parameters. MARS is a non-parametric adaptive regression procedure. It has the capability to determine the important sequence of inputs for the output. The comprehensive parameter (E) and maximum reservoir depth (H) are used as inputs for the MARS. The developed MARS gives an equation for the prediction of M. A comparative study is carried out between the developed MARS and other models and the results show that the developed MARS is a robust model for the prediction of M.</P>
Utilization of support vector machine for prediction of fracture parameters of concrete
Pijush Samui,김두기 사단법인 한국계산역학회 2012 Computers and Concrete, An International Journal Vol.9 No.3
This article employs Support Vector Machine (SVM) for determination of fracture parameters critical stress intensity factor (Ksic) and the critical crack tip opening displacement (CTODc) of concrete. 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 results are compared with a widely used Artificial Neural Network (ANN) model. Equations have been also developed for prediction of Ksic and CTODc. A sensitivity analysis has been also performed to investigate the importance of the input parameters. The results of this study show that the developed SVM is a robust model for determination of Ksic and CTODc of concrete.
Multivariate adaptive regression spline applied to friction capacity of driven piles in clay
Samui, Pijush Techno-Press 2011 Geomechanics & engineering Vol.3 No.4
This article employs Multivariate Adaptive Regression Spline (MARS) for determination of friction capacity of driven piles in clay. MARS is non-parametric adaptive regression procedure. Pile length, pile diameter, effective vertical stress, and undrained shear strength are considered as input of MARS and the output of MARS is friction capacity. The developed MARS gives an equation for determination of $f_s$ of driven piles in clay. The results of the developed MARS have been compared with the Artificial Neural Network. This study shows that the developed MARS is a robust model for prediction of $f_s$ of driven piles in clay.
Pullout capacity of small ground anchors: a relevance vector machine approach
Samui, Pijush,Sitharam, T.G. Techno-Press 2009 Geomechanics & engineering Vol.1 No.3
This paper examines the potential of relevance vector machine (RVM) in prediction of pullout capacity of small ground anchors. RVM is based on a Bayesian formulation of a linear model with an appropriate prior that results in a sparse representation. The results are compared with a widely used artificial neural network (ANN) model. Overall, the RVM showed good performance and is proven to be better than ANN model. It also estimates the prediction variance. The plausibility of RVM technique is shown by its superior performance in forecasting pullout capacity of small ground anchors providing exogenous knowledge.
Comparison of machine learning techniques to predict compressive strength of concrete
Susom Dutta,Pijush Samui,김두기 사단법인 한국계산역학회 2018 Computers and Concrete, An International Journal Vol.21 No.4
In the present study, soft computing i.e., machine learning techniques and regression models algorithms have earned much importance for the prediction of the various parameters in different fields of science and engineering. This paper depicts that how regression models can be implemented for the prediction of compressive strength of concrete. Three models are taken into consideration for this; they are Gaussian Process for Regression (GPR), Multi Adaptive Regression Spline (MARS) and Minimax Probability Machine Regression (MPMR). Contents of cement, blast furnace slag, fly ash, water, superplasticizer, coarse aggregate, fine aggregate and age in days have been taken as inputs and compressive strength as output for GPR, MARS and MPMR models. A comparatively large set of data including 1030 normalized previously published results which were obtained from experiments were utilized. Here, a comparison is made between the results obtained from all the above mentioned models and the model which provides the best fit is established. The experimental results manifest that proposed models are robust for determination of compressive strength of concrete.
Reliability analysis of simply supported beam using GRNN, ELM and GPR
Jagan J,Pijush Samui,김두기 국제구조공학회 2019 Structural Engineering and Mechanics, An Int'l Jou Vol.71 No.6
This article deals with the application of reliability analysis for determining the safety of simply supported beam under the uniformly distributed load. The uncertainties of the existing methods were taken into account and hence reliability analysis has been adopted. To accomplish this aim, Generalized Regression Neural Network (GRNN), Extreme Learning Machine (ELM) and Gaussian Process Regression (GPR) models are developed. Reliability analysis is the probabilistic style to determine the possibility of failure free operation of a structure. The application of probabilistic mathematics into the quantitative aspects of a structure and improve the qualitative aspects of a structure. In order to construct the GRNN, ELM and GPR models, the dataset contains Modulus of Elasticity (E), Load intensity (w) and performance function () in which E and w are inputs and is the output. The achievement of the developed models was weighed by various statistical parameters; one among the most primitive parameter is Coefficient of Determination (R2) which has 0.998 for training and 0.989 for testing. The GRNN outperforms the other ELM and GPR models. Other different statistical computations have been carried out, which speaks out the errors and prediction performance in order to justify the capability of the developed models.
Roshni, Thendiyath,K., Md. Sajid,Samui, Pijush Techno-Press 2017 Ocean systems engineering Vol.7 No.4
Higher prediction efficacy is a very challenging task in any field of engineering. Due to global warming, there is a considerable increase in the global sea level. Through this work, an attempt has been made to find the sea level variability due to climate change impact at Haldia Port, India. Different statistical downscaling techniques are available and through this paper authors are intending to compare and illustrate the performances of three regression models. The models: Wavelet Neural Network (WNN), Minimax Probability Machine Regression (MPMR), Feed-Forward Neural Network (FFNN) are used for projecting the sea level variability due to climate change at Haldia Port, India. Model performance indices like PI, RMSE, NSE, MAPE, RSR etc were evaluated to get a clear picture on the model accuracy. All the indices are pointing towards the outperformance of WNN in projecting the sea level variability. The findings suggest a strong recommendation for ensembled models especially wavelet decomposed neural network to improve projecting efficiency in any time series modeling.