Modeling the cyclic swelling pressure of mudrock using artificial neural networks

Moosavi, M.; Yazdanpanah, Mohammad Javad; Doostmohammadi, R.

doi:10.1016/j.enggeo.2006.07.001

Cited by 23 publications

(8 citation statements)

References 13 publications

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“…When the minimal error is obtained, the process is terminated. If the minimal error is not obtained, network changes the weights and biases and continues the process using new values (Basma & Kallas, 2004;Moosavi, Yazdanpanah, & Doostmohammadi, 2006). The most widely-used training algorithm is the feed-forward, multilayer perceptrons trained by back-propagation algorithms based on gradient descent method (FFBP).…”

Section: Overview Of Annmentioning

confidence: 99%

Modeling of the angle of shearing resistance of soils using soft computing systems

Kayadelen

Günaydın

Fener

et al. 2009

Expert Systems with Applications

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Section: Overview Of Annmentioning

confidence: 99%

Modeling of the angle of shearing resistance of soils using soft computing systems

Kayadelen

Günaydın

Fener

et al. 2009

Expert Systems with Applications

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“…This process is continued, until a particular input captures to their output (i.e., target) or as far as the lowest possible error can be obtained by using an error criterion. In other words the network training is the determination of the weights and the biases [9,10]. An ANN model can be differently composed in terms of architecture, learning rule and self organization.…”

Section: View Of the Artificial Neural Networkmentioning

confidence: 99%

Prediction of California bearing ratio (CBR) of fine grained soils by AI methods

Taşkıran

2010

Advances in Engineering Software

108

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“…target) or as far as the lowest possible error can be obtained by using an error criterion. In other words, the network training is the determination of weights and biases [29,30]. An ANN model can be differently composed in terms of architecture, learning rule and self-organization.…”

Section: View Of the Artificial Neural Networkmentioning

confidence: 99%

Estimation of effective stress parameter of unsaturated soils by using artificial neural networks

Kayadelen

2007

Num Anal Meth Geomechanics

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SUMMARYGreat efforts are required for determination of the effective stress parameter , applying the unsaturated testing procedure, since unsaturated soils that have the three-phase system exhibit complex mechanical behavior. Therefore, it seems more reasonable to use the empirical methods for estimation of . The objective of this study is to investigate the practicability of using artificial neural networks (ANNs) to model the complex relationship between basic soil parameters, matric suction and the parameter . Five ANN models with different input parameters were developed. Feed-forward back propagation was applied in the analyses as a learning algorithm. The data collected from the available literature were used for training and testing the ANN models. Furthermore, unsaturated triaxial tests were carried out under drained condition on compacted specimens. ANN models were validated by a part of data sets collected from the literature and data obtained from the current study, which were not included in the training phase. The analyses showed that the results obtained from ANN models are in satisfactory agreement with the experimental results and ANNs can be used as reliable tool for prediction of .

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Modeling the cyclic swelling pressure of mudrock using artificial neural networks

Cited by 23 publications

References 13 publications

Modeling of the angle of shearing resistance of soils using soft computing systems

Modeling of the angle of shearing resistance of soils using soft computing systems

Prediction of California bearing ratio (CBR) of fine grained soils by AI methods

Estimation of effective stress parameter of unsaturated soils by using artificial neural networks

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