Lined channels with trapezoidal, rectangular and triangular sections are the most common manmade canals in practice. Since the construction cost plays a key role in water conveyance projects, it has been considered as the prominent factor in optimum channel designs. In this study, artificial neural networks (ANN) and genetic programming (GP) are used to determine optimum channel geometries for trapezoidal-family cross sections. For this purpose, the problem statement is treated as an optimization problem whose objective function and constraint are earthwork and lining costs and Manning’s equation, respectively. The comparison remarkably demonstrates that the applied artificial intelligence (AI) models achieved much closer results to the numerical benchmark solutions than the available explicit equations for optimum design of lined channels with trapezoidal, rectangular and triangular sections. Also, investigating the average of absolute relative errors obtained for determination of dimensionless geometries of trapezoidal-family channels using AI models shows that this criterion will not be more than 0.0013 for the worst case, which indicates the high accuracy of AI models in optimum design of trapezoidal channels.

中文翻译：

---

计算衬砌渠道最佳性能的人工智能模型评估

具有梯形、矩形和三角形截面的内衬渠道是实践中最常见的人造渠道。由于建设成本在输水工程中起着关键作用，因此被认为是优化渠道设计的重要因素。在这项研究中，人工神经网络 (ANN) 和遗传编程 (GP) 用于确定梯形族横截面的最佳通道几何形状。为此，将问题陈述视为优化问题，其目标函数和约束分别为土方工程和衬砌成本以及曼宁方程。比较显着地表明，与用于优化设计梯形、矩形和三角形截面的内衬通道的可用显式方程相比，应用的人工智能 (AI) 模型获得了更接近于数值基准解的结果。此外，调查使用 AI 模型确定梯形族通道的无量纲几何所获得的绝对相对误差的平均值表明，该标准在最坏情况下不会超过 0.0013，这表明 AI 模型在优化设计中的准确性很高。梯形通道。