Abstract
Labyrinth weirs provide an economic option for flow control structures in a variety of applications, including as spillways at dams. The cycles of labyrinth weirs are typically placed in a linear configuration. However, numerous projects place labyrinth cycles along an arc to take advantage of reservoir conditions and dam alignment, and to reduce construction costs such as narrowing the spillway chute. Practitioners must optimize more than 10 geometric variables when developing a head–discharge relationship. This is typically done using the following tools: empirical relationships, numerical modeling, and physical modeling. This study applied a new tool, machine learning, to the analysis of the geometrically complex arced labyrinth weirs. In this work, both neural networks (NN) and random forests (RF) were employed to estimate the discharge coefficient for this specific type of weir with the results of physical modeling experiments used for training. Machine learning results are critiqued in terms of accuracy, robustness, interpolation, applicability, and new insights into the hydraulic performance of arced labyrinth weirs. Results demonstrate that NN and RF algorithms can be used as a unique expression for curve fitting, although neural networks outperformed random forest when interpolating among the tested geometries.
Highlights
IntroductionWater is an unevenly distributed global resource
Models were presented to estimate the discharge of arced labyrinth spillways based on two machine learning algorithms and experimental data for 10 ferent geometric configurations
Both algorithms may obtain a unique mapping that relates discharge to hydraulic head and the geometry of the tested configurations. This is an alternative to other commonly used methods such as curve-fitting experimental data, as it avoids the iterative process of selecting terms and the need to handle complex expressions. The analysis of these models is more complex, some tools are available to obtain an estimate of the effect of each input variable in the system response, which can be useful for the design of laboratory test campaigns
Summary
Water is an unevenly distributed global resource. Many communities rely heavily on water distribution systems and key elements in that system—hydraulic structures—for water supply and flood control. These communities range in size and demand, from small agrarian pueblos (e.g., areas in northern Peru, southern Africa, eastern Asia) to large metropolises (e.g., Las Vegas would not exist without Hoover Dam) [1]. Water sources may be within a short walk, or as far as many kilometers away with mountains in between. Communities require infrastructure such as dams and levees to store precipitation in dry periods and to provide flood protection in periods of excess [2]
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