Journal ArticleDOI
Site Characterization Model Using Artificial Neural Network and Kriging
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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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A Three-Dimensional Geotechnical Spatial Modeling Method for Borehole Dataset Using Optimization of Geostatistical Approaches
TL;DR: In this paper, a geotechnical three-dimensional spatial modeling was implemented using an optimized geostatistical interpolation approach at a bridge construction site in the south-central part of the Korean peninsula.
Journal ArticleDOI
Reliability Analysis of Settlement of Pile Group in Clay Using LSSVM, GMDH, GPR
Manish Kumar,Pijush Samui +1 more
TL;DR: The paper proposes least square support vector machine (LSSVM), The Group Method of Data Handling (GMDH) and Gaussian process regression (GPR) based reliability analysis of pile group resting on cohesive soil.
Journal ArticleDOI
Application of neural networks for the reliability design of a tunnel in karst rock mass
TL;DR: In this article, the authors proposed a solution to overcome time-consuming numerical analysis for the evaluation of the impact of tunnel construction in a complex karst environment by implementing Monte Carlo Simulation (MCS) using a neural network (NN) tool.
Journal ArticleDOI
Geostatistical interpolation for modelling SPT data in northern Izmir
TL;DR: In this article, the corrected Standard Penetration Test (SPT) values in Karsiyaka city center by kriging approach were estimated at depths of 3, 6, 9, 13.5, 18 and 25 m.
Journal ArticleDOI
Surrogate regression modelling for fast seismogram generation and detection of microseismic events in heterogeneous velocity models
Saptarshi Das,Xi Chen,Michael P. Hobson,Suhas Phadke,Bertwim van Beest,Jeroen Goudswaard,Detlef Hohl +6 more
TL;DR: In this paper, the Wilkes high performance GPU computing service at the University of Cambridge has been used in this work, which has been supported by the Shell Projects and Technology (SPT).
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.
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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.