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Generalized Regression Neural Networks and Feed Forward Neural Networks for prediction of scour depth around bridge piers

机译:广义回归神经网络和前馈神经网络用于预测桥墩周围的冲刷深度

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摘要

In this study, Generalized Regression Neural Networks (GRNN) and Feed Forward Neural Networks (FFNN) approaches are used to predict the scour depth around circular bridge piers. Hundred and sixty five data collected from various experimental studies, are used to predict equilibrium scour depth. The model consisting of the combination of dimensional data involving the input variables is constructed. The performance of the models in training and testing sets are compared with observations. Then, the model is also tested by Multiple Linear Regression (MLR) and empirical formula. The results of all approaches are compared in order to get more reliable comparison. The results indicated that GRNN can be applied successfully for prediction of scour depth around circular bridge piers.
机译:在这项研究中,广义回归神经网络(GRNN)和前馈神经网络(FFNN)方法用于预测圆形桥墩周围的冲刷深度。从各种实验研究中收集的165个数据用于预测平衡冲刷深度。构建了包含涉及输入变量的维数据组合的模型。将模型在训练和测试集中的性能与观察结果进行比较。然后,还通过多元线性回归(MLR)和经验公式对模型进行了测试。比较所有方法的结果,以获得更可靠的比较。结果表明,GRNN可以成功地应用于圆形桥墩冲深的预测。

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