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Journal ArticleDOI

Site Characterization Model Using Artificial Neural Network and Kriging

01 Oct 2010-International Journal of Geomechanics (American Society of Civil Engineers)-Vol. 10, Iss: 5, pp 171-180
TL;DR: 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...
Citations
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Journal ArticleDOI
TL;DR: In this paper, support vector machine regression (SVR) has been applied to evaluate accurate values of lime-treated pavement layers' resilient modulus (MR) and polynomial kernel, radial basis function, and linear kernel as three different kernels of SVR were used to predict the MR.
Abstract: The resilient modulus (MR) plays a crucial role in mechanistic–empirical design such that acquiring the MR of lime-treated pavement layers seems to be necessary, because the use of lime materials in road projects is generally established. However, because of the complexity of and time and equipment requirements for repeated and cyclic load testing, several methods have been proposed to apply. In this paper, the novel artificial intelligence algorithm called support vector machine regression (SVR) has been applied to evaluate accurate values of lime-treated pavement layers’ MR. Moreover, polynomial kernel, radial basis function, and linear kernel as three different kernels of SVR were used to predict the MR of lime-treated subgrade soil. To create the model and validate the algorithm’s performance, approximately 75% of the data was selected as training data sets, and the remaining ones were applied as testing data sets. For this study, the obtained results indicate that developed SVR models produce...

12 citations

Journal ArticleDOI
TL;DR: The paper concludes that the performance of RVM, GP and ANFIS were excellent while that of GRNN was poor.
Abstract: Considering the highly variable nature of soil, reliability analysis of pile foundation is being explored in the modern scientific era. The paper investigates the application of relevance vector machines (RVM), generalized regression neural network (GRNN), genetic programming (GP) and adaptive-network-based fuzzy inference (ANFIS) in reliability analysis of settlement of pile group. The simulation is checked using Monte Carlo simulation (M-C). The performance of models is ascertained using various performance parameters and Taylor diagrams. The normality and homogeneity in performance of the models is tested by carrying out Anderson–Darling (AD) test and Mann–Whitney U (M–W) test, respectively. The paper concludes that the performance of RVM, GP and ANFIS were excellent while that of GRNN was poor.

11 citations

Journal ArticleDOI
Changyu Jin1, Yu Lu1, Tao Han1, Chen Tianyu1, Cui Jianxin1, Dongxu Cheng1 
TL;DR: In this article, uncertainties in the geological evolution process, engineering geological conditions, and nonuniqueness of the solution for in situ stress regression analysis, stress field regressions are considered.
Abstract: Due to uncertainties in the geological evolution process, engineering geological conditions, and nonuniqueness of the solution for in situ stress regression analysis, stress field regressi...

10 citations

Journal ArticleDOI
TL;DR: In this article, the authors presented the development of next-generation prediction models for the flow number of dense asphalt-aggregate mixtures via an innovative machine learning approach, using linear genetic programming (LGP) and artificial neural network (ANN).
Abstract: This paper presents the development of next-generation prediction models for the flow number of dense asphalt–aggregate mixtures via an innovative machine learning approach. New nonlinear models were developed to predict the flow number using two robust machine learning techniques, called linear genetic programming (LGP) and artificial neural network (ANN). The flow number of Marshall specimens was formulated in terms of percentages of coarse aggregate, filler, bitumen, air voids, voids in mineral aggregate, and Marshall quotient. An experimental database containing 118 test results for Marshall specimens was used for the development of the models. Validity of the models was verified using parts of laboratory data that were not involved in the calibration process. The statistical measures of coefficient of determination, coefficient of efficiency, root-mean squared error, and mean absolute error were used to evaluate the performance of the models. Further, a multivariable least-squares regression ...

10 citations

Journal ArticleDOI
TL;DR: In this paper, four metamodels (Adaptive Neuro Fuzzy Inference System, Gaussian Process Regression, Multivariate Adaptive Regression Spline and Generalized Regression Neural Network) based on First Order Second Moment Method (FOSM) has been used to determination of reliability index (β) of an infinite slope.
Abstract: Reliability analysis is an important work to determine the stability of an infinite slope. In this study, four metamodels (Adaptive Neuro Fuzzy Inference System, Gaussian Process Regression, Multivariate Adaptive Regression Spline and Generalized Regression Neural Network) based on First Order Second Moment Method (FOSM) has been used to determination of reliability index (β) of an infinite slope. An example has been taken to show the working procedure of the adopted techniques. The results shows the developed models overcome the limitations of the FOSM. A comparative study has been done between the developed models.

10 citations

References
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Journal ArticleDOI
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.

14,937 citations

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8,937 citations

Journal ArticleDOI
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.
Abstract: The first of these questions is in the province of sensory physiology, and is the only one for which appreciable understanding has been achieved. 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. With regard to the second question, two alternative positions have been maintained. The first suggests that storage of sensory information is in the form of coded representations or images, with some sort of one-to-one mapping between the sensory stimulus

8,434 citations

Book
01 Jan 1988
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.
Abstract: The first of these questions is in the province of sensory physiology, and is the only one for which appreciable understanding has been achieved. 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. With regard to the second question, two alternative positions have been maintained. The first suggests that storage of sensory information is in the form of coded representations or images, with some sort of one-to-one mapping between the sensory stimulus

8,134 citations

Journal ArticleDOI
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.
Abstract: The Marquardt algorithm for nonlinear least squares is presented and is incorporated into the backpropagation algorithm for training feedforward neural networks. The algorithm is tested on several function approximation problems, and is compared with a conjugate gradient algorithm and a variable learning rate algorithm. It is found that the Marquardt algorithm is much more efficient than either of the other techniques when the network contains no more than a few hundred weights. >

6,899 citations