Predicting Settlement of Shallow Foundations using Neural Networks

Shahin, Mohamed A.; Maier, Holger R.; Jaksa, M.

doi:10.1061/(asce)1090-0241(2002)128:9(785)

Cited by 281 publications

(146 citation statements)

References 11 publications

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“…These methods demonstrated to be reliable to provide predictive models and due to their data-driven basis, there is no requirement to preceding knowledge of the associations of the variables [34]. Thus, ANNs do not include any pre-processed equations and the models are being trained in order to find the relationships associating a group of selected inputs to their target values [35,36].…”

Section: Ann Model Developmentmentioning

confidence: 99%

Evaluation of shear strength parameters of granulated waste rubber using artificial neural networks and group method of data handling

2018

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Utilizing rubber shreds in civil engineering industry such as geotechnical structures can accelerate generated waste tire recycling process in an economical and environmentally friendly manner. However, understanding the rubber grains strength parameters is required for engineering designs and can be acquired through experimental tests. In this study, small and large direct shear test was implemented to specify shear strength parameters of five rubber grains group which are different in gradation and size. Moreover, artificial neural networks (ANN) are developed based on the test results and optimized networks which best captured the shear stress (τ), and vertical strain (ε v ) behavior of rubbers, are introduced. Additionally, a prediction model using the combinatorial algorithm in group method of data handling (GMDH) is proposed for the shear strength and vertical strain in the arrangement of closed-form equations. The performance and accuracies of the proposed models were checked using correlation coefficient (R) between the experimental and predicted data and the existing mean square error (MSE) was evaluated. R-values of the modeled τ and ε v are equal to 0.9977 and 0.9994 for ANN, and 0.9862 and 0.9942 for GMDH models, respectively. The GMDH proposed models are presented as comparatively simple explicit mathematical equations for further applications.

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Section: Ann Model Developmentmentioning

confidence: 99%

Evaluation of shear strength parameters of granulated waste rubber using artificial neural networks and group method of data handling

2018

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“…In this study, we have used 70% of the data for training. The statistical consistency of training and testing datasets improves the performance of the ANN model and later helps in evaluating them better (Shahin et al, 2000). CSR has been used as an input parameter in MODEL I. CSR has been calculated from the following formula (Seed and Idriss, 1971),…”

Section: Ann Modelmentioning

confidence: 99%

Machine learning modelling for predicting soil liquefaction susceptibility

Samui

Sitharam

2011

Nat. Hazards Earth Syst. Sci.

134

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Abstract. This study describes two machine learning techniques applied to predict liquefaction susceptibility of soil based on the standard penetration test (SPT) data from the 1999 Chi-Chi, Taiwan earthquake. The first machine learning technique which uses Artificial Neural Network (ANN) based on multi-layer perceptions (MLP) that are trained with Levenberg-Marquardt backpropagation algorithm. The second machine learning technique uses the Support Vector machine (SVM) that is firmly based on the theory of statistical learning theory, uses classification technique. ANN and SVM have been developed to predict liquefaction susceptibility using corrected SPT [(N 1 ) 60 ] and cyclic stress ratio (CSR). Further, an attempt has been made to simplify the models, requiring only the two parameters [(N 1 ) 60 and peck ground acceleration (a max /g)], for the prediction of liquefaction susceptibility. The developed ANN and SVM models have also been applied to different case histories available globally. The paper also highlights the capability of the SVM over the ANN models.

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“…The back-propagation neural network has been applied with great success to model many phenomena in the field of geotechnical and geoenvironmental engineering [27][28][29][30][31]. Each hidden and output neuron processes its input(s) by multiplying each by its weight, summing the product, and then processing the sum using a nonlinear transfer function, (also called an 'activation function'), to obtain the desired result.…”

Section: Details Of the Neural Networkmentioning

confidence: 99%