Abstract
In this paper, Artificial Neural Networks (ANNs) are applied to the prediction of lateral behavior of single and group piles. The results of neural networks are compared with the measured data from the model tests. A series of model tests were performed with single and group piles in the Nak-Dong River Sand. For the verification of applicability of BPNN (Back Propagation Neural Network), the Modified BPNN and SNN (Sequential Neural Network), a total of 200 model test results for single and group piles were used. Also, in this study, the structure of neural network with one input layer-two hidden layerone output layer was used. The number of neuron for each hidden layer determined to be 30 from the results of model test and the learning rate determined to be 0.9 to optimize network learning. Compared with BPNN, the learning epoch of the modified BPNN for the same learning pattern is reduced to 88% at the maximum and the convergence to a global minimum is guaranteed. The investigation confirmed that a SNN with feedback is more effective than a conventional ANN without feedback to simulate the lateral behavior for single and group piles. It is concluded from this study that artificial neural ne tworks based on pile-soil interaction models can be developed by properly predicting and learning algorithms based on a comprehensive data set, and that useful inferences can be made from such models.
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The manuscript for this paper was submitted for review on March 2, 2001.
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Kim, B.T., Kim, Y.S. & Lee, S.H. Prediction of lateral behavior of single and group piles using artificial neural networks. KSCE J Civ Eng 5, 185–198 (2001). https://doi.org/10.1007/BF02829074
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DOI: https://doi.org/10.1007/BF02829074