Das S.K

Samui P

Sabat A.K.

Vellore Institute of Technology (VIT) is a private university located in&nbsp;Tamil Nadu, India. Founded in 1984, as Vellore Engineering College, the institution offers 20 undergraduate, 34 postgraduate, four integrated and four research programs. It has campuses in Vellore, Amravati, Bhopal and Chennai.

VIT is one of the top ranked private universities in India according to NIRF, THE and QS Rankings.&nbsp;Govt. of India has recognized&nbsp;VIT, Vellore as an&nbsp;Institution of Eminence. This has allowed VIT to take independent quality initiatives and move up in world ranking.

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VIT University

International Journal of Geomechanics

Marine Georesources & Geotechnology

Uplift Capacity of Suction Caisson in Clay Using Artificial Intelligence Techniques

European Journal of Environmental and Civil Engineering

SPT-based liquefaction potential assessment by relevance vector machine approach

Prediction of Field Hydraulic Conductivity of Clay Liners Using an Artificial Neural Network and Support Vector Machine

Footings placed on or near a slope, the slope face act as a finite boundary that leads to an inappropriate and inadequate development of the plastic region of failure as the foundation approaches the limit state under the applied loading. Depending upon the position of the footing and steepness of the slope, the above-mentioned phenomena might lead to considerable decrease in the bearing capacity of the foundation. A sequence of finite element analysis has been carried out using Plaxis 2D v2015.02 to inspect the ultimate bearing capacity of strip footings placed at the crest of the c–φ soil slope. The effect of various geo-parameters namely internal angle of friction (φ), cohesion (c), setback distance (b), width of footing (B), slope angle (β) and embedment depth of footing (D f ) on the ultimate bearing capacity of the footing have been investigated and the outputs obtained are appropriately elucidated. In addition, large database of numerically simulated ultimate bearing capacity has been considered to develop and verify the ANN model to establish the relative importance of the input parameters. © 2019, The Author(s).

International Journal of Geo-Engineering

Finite element investigation and ANN-based prediction of the bearing capacity of strip footings resting on sloping ground

2012 International Joint Conference on Service Sciences

Copyright (c) 2012 by The Institute of Electrical and Electronics Engineers, Inc. All rights reserved. [Copyright page]

Human Resource Management Journal

LIST OF REVIEWERS OCTOBER 2010-SEPTEMBER 2011

This study deals with development of artificial neural networks (ANNs) and multiple regression analysis (MRA) models for determining hydraulic conductivity of fine-grained soils. To achieve this, conventional falling-head tests, oedometer falling-head tests, and centrifuge tests were conducted on silty sand and marine clays compacted at different dry densities and moisture contents. Further, results obtained from ANN and MRA models were compared vis-à-vis experimental results. The performance indices such as the coefficient of determination, root mean square error, mean absolute error, and variance were used to assess the performance of these models. The ANN models exhibit higher prediction performance than the MRA models based on their performance indices. It has been demonstrated that the ANN models developed in the study can be employed for determining hydraulic conductivity of compacted fine-grained soils quite efficiently. © 2009 NRC Canada.

Canadian Geotechnical Journal

Artificial neural network (ANN) models for determining hydraulic conductivity of compacted fine-grained soils

Engineering Geology

Prediction of residual friction angle of clays using artificial neural network

Journal	Data powered by TypesetInternational Journal of Geomechanics
Publisher	Data powered by TypesetAmerican Society of Civil Engineers (ASCE)
ISSN	1532-3641
Open Access	0