利用M5树及ANN-PSO模拟桥墩周围清水冲刷深度

IF 2.1 4区 环境科学与生态学 Q2 ENGINEERING, CIVIL
Amarjeet Kumar, Anubhav Baranwal, B. S. Das
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引用次数: 0

摘要

冲刷是指河流中的河床沉积物在桥台或桥墩周围被侵蚀的过程。许多经验模型可用于估算不同流量、几何形状和床层粗糙度条件下的冲刷深度。然而,对于大范围的输入参数,它们都不能提供更好的冲刷深度估计。因此,在本文中,通过考虑广泛的数据集,使用M5树和混合人工神经网络(ANN)-粒子群优化(PSO)技术对桥墩周围的冲刷深度进行了建模。从文献中收集清水冲刷(CWS)数据集,选取5个不同的无量纲影响参数作为输入参数,对冲刷深度进行建模。采用Gamma检验(GT)选择最佳的输入参数组合。基于最小伽玛值和v比,从26种不同的CWS深度建模输入组合中选择了4种。根据统计测量,M5树模型比经验方法更能预测冲刷深度。此外,所建立的ANN-PSO模型适用于矩形和圆形桥墩冲刷深度的确定。将所建立的模型与已有的模型进行了比较,结果令人满意。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Modelling of clear water scour depth around bridge piers using M5 tree and ANN-PSO
Scouring refers to the process by which bed sediment in a river is eroded around the periphery of a bridge abutment or pier. Many empirical models are available to estimate the scour depth for different flow, geometry, and bed roughness condition. However, none of them provide a better estimation of scour depth for a wide range of input parameters. Thus, in this paper, the scour depth around bridge piers has been modelled using M5 tree and hybrid artificial neural network (ANN)-particle swarm optimisation (PSO) techniques by considering the wide range of datasets. The clear-water scouring (CWS) datasets are collected from the literature and five different non-dimensional influencing parameters are selected as input parameters to model the scour depth. A Gamma test (GT) was performed to choose the best input parameter combinations. Based on the lowest gamma value and V-ratio, 4 out of 26 distinct input combinations for CWS depth modelling were chosen in the GT. According to statistical measures, the proposed M5 tree model predicts scour depth better than empirical approaches. Additionally, the developed ANN-PSO model is suitable for determining scour depth in both rectangular and circular shapes of piers. The results of both developed models are compared with other existing models and found to be satisfactory.
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来源期刊
CiteScore
4.10
自引率
21.10%
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审稿时长
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