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Computation of Design Coefficients in Ogee-crested Spillway Structure Using GEP and Regression Models

机译:基于GEP和回归模型的含盖溢洪道结构设计系数计算

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The ogee-crested spillway is a passage in a dam through which the design flood could be disposed of safely to the downstream. Spillways of improper design or insufficient capacities have caused failures of dams. Therefore, the spillway must be hydraulically and structurally adequate. This paper presents Gene-Expression Programming (GEP) models as an alternative approach to prediction of design coefficients in ogee-crested spillway structure. New formulations for prediction of design coefficient are developed using GEP and regression models. The performance of GEP was found to be satisfactory and encouraging when compared with regression model in predicting of design coefficient. This capability of GEP makes it unique and more effective when compared with regression models evaluated in this paper. The superior performance of GEP is attributed to the powerful Artificial Intelligence (AI) techniques for computer learning inspired by natural evolution to find the appropriate mathematical model (expression) to fit a set of fits. This study highlights the utility of AI-based models with a view to increase their usage by engineers and planners working on spillway design problems.
机译:含石膏的溢洪道是大坝中的一条通道,通过该通道可以将设计洪水安全地排放到下游。设计不当或容量不足的溢洪道已导致大坝倒塌。因此,溢洪道必须在液压和结构上足够。本文介绍了基因表达编程(GEP)模型,作为预测含油砂顶溢洪道结构中设计系数的替代方法。使用GEP和回归模型开发了用于预测设计系数的新公式。与回归模型相比,GEP的性能令人满意,令人鼓舞。与本文评估的回归模型相比,GEP的这种功能使其具有独特性和有效性。 GEP的卓越性能归功于强大的人工智能(AI)技术,该技术是受自然进化启发而找到合适的数学模型(表达式)以适合一组拟合的,从而可进行计算机学习。这项研究强调了基于AI的模型的实用性,以期增加处理溢洪道设计问题的工程师和计划人员的使用率。

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