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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Book Chapter
Application of Data Mining techniques for the development of new geomechanical characterization models for rock masses
Tiago F. S. Miranda,Luis Sousa +1 more
Journal ArticleDOI
Modeling of Elastic Modulus of Jointed Rock Mass: Gaussian Process Regression Approach
TL;DR: In this paper, the capability of Gaussian process regression (GPR) for determination of the elastic modulus (Ej) of jointed rock masses is examined. And the results show that the developed GPR is a promising tool for the prediction of the Ej of jointing rock masses.
Journal ArticleDOI
ANN and Neuro-Fuzzy Modeling for Shear Strength Characterization of Soils
TL;DR: In this paper, the authors examined the performance of ANN and ANFIS for estimating the shear strength parameters of c − − ǫ φ soil using a matrix of one hundred twelve datasets collected using in situ and laboratory tests.
Journal ArticleDOI
Hybrid ELM and MARS-Based Prediction Model for Bearing Capacity of Shallow Foundation
Manish Kumar,Vinay Kumar,Rahul Das Biswas,Pijush Samui,Mosbeh R. Kaloop,Majed Alzara,Ahmed Yosri +6 more
TL;DR: It is concluded that AI-based models are robust and hybridization of regression models with optimization techniques should be encouraged in further research.
Journal ArticleDOI
Risk-based characterisation of an urban building site
TL;DR: In this paper, a risk-based site characterisation scheme was developed for urban sites based on the effects of large-scale, geological spatial variability by using fragility curves to quantify these effects.
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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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.