Artificial neural network for stress–strain behavior of sandy soils: Knowledge based verification

Banimahd, Meysam; Yasrobi, S. Shahaboddin; Woodward, Peter Keith

doi:10.1016/j.compgeo.2005.06.002

Cited by 81 publications

(45 citation statements)

References 22 publications

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“…Artificial Neural Networks are computational models based on the information processing system of the human brain and nervous system [15]. They can be considered as a group of simple, highly interconnected elements that process the information by their dynamic state response to external inputs.…”

Section: Overview Of Artificial Intelligencementioning

confidence: 99%

A review of Genetic Programming and Artificial Neural Network applications in pile foundations

Fatehnia

Amirinia

2018

Geo-Engineering

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Section: Overview Of Artificial Intelligencementioning

confidence: 99%

A review of Genetic Programming and Artificial Neural Network applications in pile foundations

Fatehnia

Amirinia

2018

Geo-Engineering

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“…In this testing phase, the neural network predictions using the trained weights are compared to the target output values. The performance of the overall ANN model can be assessed by several criteria [32][33][34][35]. These criteria include coefficient of determination R 2 , root mean squared error, mean absolute error, minimal absolute error, maximum absolute error and variance account for.…”

Section: Details Of the Neural Networkmentioning

confidence: 99%

“…However, dividing the data into only two subsets may lead to model over fitting. Over fitting makes multi-layer perceptrons (MLPs) memorize training patterns in such a way that they cannot generalize well to new data [34]. As a result, the cross validation technique [42] was used as the stopping criterion in this study.…”

Section: Development Of Ann Modelsmentioning

confidence: 99%

“…Consequently, one hidden layer was used in all the models. The neural network toolbox of MATLAB7.0, a popular numerical computation and visualization software [34], was used for training and testing of MLPs. The optimum number of neurons in the hidden layer of each model was determined by varying their number by starting with a minimum of 1 and then increasing the network size in steps by adding 1 neuron each time.…”

Section: Development Of Ann Modelsmentioning

confidence: 99%

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Artificial neural network models for predicting soil thermal resistivity

Erzin

Rao

Singh

2008

International Journal of Thermal Sciences

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“…The capability of storing the learning experience and the power to capture the inherent complex relationship without any prior assumptions about the geotechnical engineering problem makes the neural network a suitable choice for modeling. Past studies [5][6][7][8][9][10][11] have demonstrated that neural network-based prediction models can be used in predicting the soil properties or behaviour. With the above in view, in the present study, a feed forward neural network based predictive model from the consolidated drained triaxial test data has been developed.…”

Section: Introductionmentioning

confidence: 99%

Prediction of Deviator Stress of Sand Reinforced with Waste Plastic Strips Using Neural Network

Dutta

Jeevanandham

2015

Int. J. of Geosynth. and Ground Eng.

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The paper presents a study conducted on sandwaste plastic strip mixture for carrying out consolidated drained triaxial compression tests and to use the experimental data in training, testing, and prediction phases of neural network-based soil models. The input variables in the developed neural network models were strip content, tensile strength of strip, thickness of the strip, elongation at failure of the strip, aspect ratio, dry unit weight of the composite specimen, confining pressure and strain at failure of the composite specimen and the output was the deviator stress. These variables were considered to construct 8-6-1 topology of neural network in the prediction of the deviator stress. Further, using the mean squared error, root mean squared error, mean absolute error, mean absolute percentage error, correlation coefficient (r) and coefficient of determination (R 2 ) for the training and testing data, the predictability of neural networks was analysed using various activation functions. The neural network model obtained had an acceptable accuracy. Sensitivity analysis revealed that the contribution of the input variables such as strip thickness, tensile strength of the strip and dry unit weight does not have much impact on the output deviator stress. After the sensitivity analysis, neural network structure was revised. The revised model having 5-4-1 topology gives a better prediction of the output deviator stress than the previous model with 8-6-1 topology. Further, the revised neural network model having 5-4-1 topology is superior to the one obtained using multiple regression analysis in predicting the output deviator stress. Finally a model equation is presented based on trained weights in the revised neural network.

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Artificial neural network for stress–strain behavior of sandy soils: Knowledge based verification

Cited by 81 publications

References 22 publications

A review of Genetic Programming and Artificial Neural Network applications in pile foundations

A review of Genetic Programming and Artificial Neural Network applications in pile foundations

Artificial neural network models for predicting soil thermal resistivity

Prediction of Deviator Stress of Sand Reinforced with Waste Plastic Strips Using Neural Network

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