Statistical and neural network assessment of the compression index of clay-bearing soils

Özer, Mustafa; Işık, Nihat; Orhan, Mehmet

doi:10.1007/s10064-008-0168-8

Cited by 53 publications

(22 citation statements)

References 24 publications

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“…Laboratory data, based on calibration chamber tests, were used to successfully train and test the neural network model. The neural network model used soil parameters as inputs and the compression index as a single output (Ozer et al, 2008;Park & Lee, 2010). The ANN models was found to give higher coefficients of correlation than empirical equations for the training and testing data, respectively, which indicated that the neural network was successful in modelling the complex relationship between the compression index and the other soil parameters.…”

Section: Properties Of Geo-materialsmentioning

confidence: 99%

Study for Application of Artificial Neural Networks in Geotechnical Problems

Park¹

2011

Artificial Neural Networks - Application

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Section: Properties Of Geo-materialsmentioning

confidence: 99%

Study for Application of Artificial Neural Networks in Geotechnical Problems

Park¹

2011

Artificial Neural Networks - Application

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“…Recently, ANNs have been found to be a useful tool to solve many problems in the field of the geotechnical engineering [3]. Since the early 1990s, ANNs have been effectively applied to almost every problem in geotechnical engineering, including constitutive modeling [5,6]; geomaterial properties [3,[7][8][9]; bearing capacity of pile [10,11]; slope stability [12][13][14][15][16]; shallow foundations [17][18][19]; liquefaction potential [20][21][22][23][24][25][26]; and tunnels and underground openings [27,28].…”

Section: Introductionmentioning

confidence: 99%

The use of neural networks for the prediction of cone penetration resistance of silty sands

Erzin

Ecemis

2016

Neural Comput & Applic

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In this study, an artificial neural network (ANN) model was developed to predict the cone penetration resistance of silty sands. To achieve this, the data sets reported by Ecemis and Karaman, including the results of three high-quality field tests, namely piezocone penetration test, pore pressure dissipation tests, and direct push permeability tests performed at 20 different locations on the northern coast of the Izmir Gulf in Turkey, have been used in the development of the ANN model. The ANN model consisted of three input parameters (relative density, fines content, and horizontal coefficient of consolidation) and a single output parameter (normalized cone penetration resistance). The results obtained from the ANN model were compared with those obtained from the field tests. It is found that the ANN model is efficient in determining the cone penetration resistance of silty sands and yields cone penetration resistance values that are very close to those obtained from the field tests. Additionally, several performance indices such as the determination coefficient, variance account for, mean absolute error, root mean square error, and scaled percent error were computed to examine the performance of the ANN model developed. The performance level attained in the ANN model shows that the ANN model developed in this study can be employed for predicting cone penetration of silty sands quite efficiently.

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“…This mathematical description can be represented by a Single [24] system of quadratic polynomials consisting of only two variables in the form of: y =G(x i ; x j ) = a 0 + a 1 x i + a 2 x j + a 3 x i x j + a 4 x 2 i + a 5 x 2 j : (8) The coe cients, a i , in Eq. (5) are calculated using regression analysis so that the di erence between the observed output y and the calculated one,ŷ, for each pair of x i and x j as input variables is minimum:…”

Section: Modelling Using a Polynomial Functionmentioning

confidence: 99%

“…n %), or in-situ void ratio (e 0 ) [12][13][14][15][16][17][18][19]. However, others recommend multiple soil-parameter models [12][13][14][20][21][22][23][24] for the estimation of C c .…”

Section: Introductionmentioning

confidence: 99%

Prediction of Compression Index of Saturated Clays (Cc) Using polynomial models

MolaAbasi¹,

Shooshpasha²,

Ebrahimi³

2016

Scientia Iranica

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Abstract. Settlement-based design for shallow foundation realizing consolidation aspect is a major task of geotechnical engineer. Compression index (C c ) from the oedometer test is used to estimate the consolidation settlement of clays. Since the determination of C c from oedometer tests is relatively time-consuming, empirical equations, based on index properties, can be useful for settlement estimation. Empirical correlations have been proposed to relate Cc of clay deposits to other soil parameters. New polynomial models are proposed for correlation. In order to assess the merits of the proposed approach, a database containing 352 data points has been compiled from case histories via geotechnical investigation sites in the province of Mazandaran, along southern shoreline of the Caspian Sea, Iran. We compare our results, involving polynomial tting with earlier results of statistical correlation relations, with other geotechnical soil properties. The predicted values using our model are checked with the measured ones to evaluate the performance of the polynomial model. The results suggest that the newly proposed approach of correlation provides a means for recognizing, more e ciently, the patterns in the data and predicting the Cc, reliably.

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Statistical and neural network assessment of the compression index of clay-bearing soils

Cited by 53 publications

References 24 publications

Study for Application of Artificial Neural Networks in Geotechnical Problems

Study for Application of Artificial Neural Networks in Geotechnical Problems

The use of neural networks for the prediction of cone penetration resistance of silty sands

Prediction of Compression Index of Saturated Clays (Cc) Using polynomial models

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