Journal ArticleDOI
Site Characterization Model Using Artificial Neural Network and Kriging
TLDR
In this article, the problem of site characterization is treated as a task of function approximation of the large existing data from standard penetration tests (SPTs) in three-dimensional subsurface of Bangalore, India.Abstract:
In this paper, the problem of site characterization is treated as a task of function approximation of the large existing data from standard penetration tests (SPTs) in three-dimensional subsurface of Bangalore, India. More than 2,700 field SPT values (N) has been collected from 766 boreholes spread over an area of 220 -km2 area in Bangalore, India. To get N corrected value ( Nc ) , N values have been corrected for different parameters such as overburden stress, size of borehole, type of sampler, length of connected rod. In three-dimensional analysis, the function Nc = Nc ( X,Y,Z ) , where X , Y , and Z are the coordinates of a point corresponds to Nc value, is to be approximated with which Nc value at any half-space point in Bangalore, India can be determined. An attempt has been made to develop artificial neural network (ANN) model using multilayer perceptrons that are trained with Levenberg-Marquardt back-propagation algorithm. Also, a geostatistical model based on ordinary kriging technique has been ad...read more
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Accurate prediction of dynamic viscosity of polyalpha-olefin boron nitride nanofluids using machine learning
TL;DR: In this article , three machine learning models, namely Support Vector Regression (SVR), Artificial Neural Networks (ANN), and Adaptive Neuro-Fuzzy Inference System (ANFIS), were compared to predict the viscosity of polyalpha-Olefin-hexagonal boron nitride (PAO-hBN) nanofluids.
Journal ArticleDOI
Data fusion of geotechnical and geophysical data for three-dimensional subsoil schematisations
TL;DR: In this paper , a data fusion methodology is presented to perform subsoil schematisation and parametrisation, making use of available geotechnical, geological and geophysical data sets, and combines them by means of machine learning algorithms (Neural Networks and Random Forest).
Journal ArticleDOI
PREDICTION OF QU AND Ru CAPACITIES OF PILE IN CLAYEY SOIL LAYER USING GEOSPATIAL ANALYSIS
TL;DR: In this article , the authors used isotropic Moran's method to predict the ultimate soil bearing (Q u ) and to determine actual load-carrying capacities (R u ) at the final set installation.
References
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Journal ArticleDOI
A logical calculus of the ideas immanent in nervous activity
Warren S. McCulloch,Walter Pitts +1 more
TL;DR: In this article, it is shown that many particular choices among possible neurophysiological assumptions are equivalent, in the sense that for every net behaving under one assumption, there exists another net which behaves under another and gives the same results, although perhaps not in the same time.
Journal ArticleDOI
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TL;DR: This article will be concerned primarily with the second and third questions, which are still subject to a vast amount of speculation, and where the few relevant facts currently supplied by neurophysiology have not yet been integrated into an acceptable theory.
Book
The perception: a probabilistic model for information storage and organization in the brain
TL;DR: The second and third questions are still subject to a vast amount of speculation, and where the few relevant facts currently supplied by neurophysiology have not yet been integrated into an acceptable theory as mentioned in this paper.
Journal ArticleDOI
Training feedforward networks with the Marquardt algorithm
TL;DR: The Marquardt algorithm for nonlinear least squares is presented and is incorporated into the backpropagation algorithm for training feedforward neural networks and is found to be much more efficient than either of the other techniques when the network contains no more than a few hundred weights.