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首页> 外文期刊>International journal of river basin management >Estimation of dimension and time variation of local scour at short abutment
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Estimation of dimension and time variation of local scour at short abutment

机译：估计短基台局部冲刷的尺寸和时间变化

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摘要

Accurate prediction of the local scour at abutments is an important criterion to design a safe depth for the bridge foundation. In this paper, the dimension and variation of local scour with time at a vertical-wall abutment were investigated experimentally under clear-water conditions. The multiple linear regression (MLR), gene expression programming (GEP) and artificial neural networks (ANNs), feed forward back propagation and radial basis function were used to predict the time variation of scour depth at a short abutment. Results indicated that the dimension of the scour hole in the x-direction ranged from 3L to 5L upstream and downstream of the abutment, respectively, and also 4L in the y-direction. Statistical analysis showed that, although the ANNs technique produced better results (R~2 = 0.997, RMSE = 0.0113 and MAE = 0.0071) in comparison with the GEP (R~2 = 0.959, RMSE = 0.068 and MAE = 0.044) and MLR techniques (R~2 = 0.958, RMSE = 0.059 and MAE = 0.041), both GEP and MLR are more practical methods. Finally, sensitivity analysis indicated that the local scour was greatly affected by the three studied parameters in the following order, time ratio (t/t_e) > abutment length ratio (L/y) ＞ velocity ratio (U/U_c).
机译：对桥台局部冲刷的准确预测是设计桥梁基础安全深度的重要标准。本文研究了在清水条件下，垂直壁基台上局部冲刷的尺寸和随时间的变化。多元线性回归（MLR），基因表达编程（GEP）和人工神经网络（ANN），前馈传播和径向基函数用于预测短基台冲刷深度的时间变化。结果表明，冲孔的尺寸在基台的上游和下游分别为3L至5L，在y方向为4L。统计分析表明，尽管与GEP（R〜2 = 0.959，RMSE = 0.068和MAE = 0.044）和MLR技术相比，人工神经网络技术产生了更好的结果（R〜2 = 0.997，RMSE = 0.0113和MAE = 0.0071）。 （R〜2 = 0.958，RMSE = 0.059，MAE = 0.041），GEP和MLR都是更实用的方法。最后，敏感性分析表明，局部冲刷受三个研究参数的影响程度依次为：时间比（t / t_e）>基台长度比（L / y）＞速度比（U / U_c）。

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