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...
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TL;DR: In this article, a prediction model that can resemble the full load-settlement response of drilled shafts (bored piles) subjected to axial loading was developed, which was calibrated and validated using several in situ full-scale pile load tests, as well as cone penetration test (CPT) data.
Abstract: The design of pile foundations requires good estimation of the pile load-carrying capacity and settlement. Design for bearing capacity and design for settlement have been traditionally carried out separately. However, soil resistance and settlement are influenced by each other, and the design of pile foundations should thus consider the bearing capacity and settlement inseparably. This requires the full load–settlement response of piles to be well predicted. However, it is well known that the actual load–settlement response of pile foundations can be obtained only by load tests carried out in situ, which are expensive and time-consuming. In this paper, recurrent neural networks (RNNs) were used to develop a prediction model that can resemble the full load–settlement response of drilled shafts (bored piles) subjected to axial loading. The developed RNN model was calibrated and validated using several in situ full-scale pile load tests, as well as cone penetration test (CPT) data. The results indica...
21 citations
TL;DR: In this paper, a general regression neural network (GRNN) is developed for predicting soil type and standard penetration test (SPT) N (standard penetration resistance) values based on SPT test results.
Abstract: In this study, a general regression neural network (GRNN) is developed for predicting soil type and standard penetration test (SPT) N (standard penetration resistance) values based on SPT test results. It focuses on soils mainly in Khulna City, Bangladesh that comprise fine-grained alluvium deposits of mostly silt and clay with some organic content and sand. A detailed geological and geotechnical investigation of the city and its surroundings was conducted to generalize the subsoil condition of the study area based on soil type and SPT values. The investigation results showed that the city is divided into four geological units and three geotechnical zones. To develop the GRNN model, more than 2326 field SPT values ( N ) have been collected from 42 clusters containing 143 boreholes spread over an area of 37 km 2 . Two trained models were developed: initially the borehole locations were trained with the soil types and after that the borehole location-soil types were trained with the N c values. The model prediction was compared with the borehole data and the results showed that the GRNN model predicts well compared with the actual site investigation data. Therefore, this model can be used for future planning and expansion of the city.
20 citations
Cites background from "Site Characterization Model Using A..."
...Nc values are also correlated with many other geotechnical properties such as angle of internal friction, shear wave velocity and cone tip resistance (Samui & Sitharam 2010)....
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TL;DR: In this article, the feasibility of using artificial neural networks (ANNs) for modeling the monotonic behaviors of various angular and rounded rockfill materials is investigated, and the ANN architecture is obtained by a trial-and-error approach in accordance with error indexes and real data.
Abstract: In this paper, the feasibility of developing and using artificial neural networks (ANNs) for modeling the monotonic behaviors of various angular and rounded rockfill materials is investigated. The database used for development of the ANN models is comprised of a series of 82 large-scale, drained triaxial tests. The deviator stress-volumetric strain versus axial strain behaviors were first simulated by using ANNs. A feedback model using multilayer perceptrons for predicting drained behavior of rockfill materials was developed in the MATLAB environment, and the optimal ANN architecture was obtained by a trial-and-error approach in accordance with error indexes and real data. Reasonable agreement between the simulated behaviors and the test results was observed, indicating that the ANNs are capable of capturing the behavior of rockfill materials. The ability of ANNs to predict the constitutive hardening-soil model parameters, residual deviator stresses, and corresponding volumetric strain was also in...
19 citations
TL;DR: A modified Lloyd algorithm is developed to generate CVT-based site investigation programs, which are applicable to arbitrary site geometries and arbitrary numbers of investigation locations.
19 citations
TL;DR: The results show that the Bayesian inference method can estimate variogram parameters and the coefficients of trend function accurately and the model uncertainty of the variograms, the predictive and total uncertainty of prediction all decrease as the sampling density increases at NS31 and NS12.
Abstract: When geotechnical properties show a trend with spatial coordinates, estimation of a variogram model is a challenging task. In the previous studies, the trend-removal method based on ordinary least-...
17 citations
Cites background from "Site Characterization Model Using A..."
...…engineering to consider natural spatial variability based on limited measurements (Nobre and Sykes 1992; Lark 2000a; Gringarten and Deutsch 2001; Oh and Kwon 2001; Murakami et al. 2006; Samui and Sitharam 2010; Zhang, Huang, and Phoon 2013; Li et al. 2016; Al-Bittar, Soubra, and Thajeel 2018)....
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References
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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
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
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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
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