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Estimation of pile group scour using adaptive neuro-fuzzy approach

机译：自适应神经模糊方法估算桩群冲刷量

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摘要

An accurate estimation of scour depth around piles is important for coastal and ocean engineers involved in the design of marine structures. Owing to the complexity of the problem, most conventional approaches are often unable to provide sufficiently accurate results. In this paper, an alternative attempt is made herein to develop adaptive neuro-fuzzy inference system (ANFIS) models for predicting scour depth as well as scour width for a group of piles supporting a pier. The ANFIS model provides the system identification and interpretability of the fuzzy models and the learning capability of neural networks in a single system. Two combinations of input data were used in the analyses to predict scour depth: the first input combination involves dimensional parameters such as wave height, wave period, and water depth, while the second combination contains nondimensional numbers including the Reynolds number, the Keulegan-Carpenter number, the Shields parameter and the sediment number. The test results show that ANFIS performs better than the existing empirical formulae. The ANFIS predicts scour depth better when it is trained with the original (dimensional) rather than the nondimensional data. The depth of scour was predicted more accurately than its width. A sensitivity analysis showed that scour depth is governed mainly by the Keulegan-Carpenter number, and wave height has a greater influence on scour depth than the other independent parameters.
机译：对于参与海洋结构设计的沿海和海洋工程师而言，准确估算桩周围的冲刷深度非常重要。由于问题的复杂性，大多数常规方法通常无法提供足够准确的结果。在本文中，本文进行了另一种尝试来开发自适应神经模糊推理系统（ANFIS）模型，以预测支撑墩的一组桩的冲刷深度和冲刷宽度。 ANFIS模型提供了模糊模型的系统识别和解释能力，以及单个系统中神经网络的学习能力。分析中使用了两种输入数据组合来预测冲刷深度：第一种输入组合涉及尺寸参数，例如波高，波浪周期和水深，而第二种组合包含无维数，包括雷诺数，Keulegan-Carpenter号，Shields参数和沉积物号。测试结果表明，ANFIS的性能优于现有的经验公式。当使用原始（尺寸）而非无量纲数据进行训练时，ANFIS可以更好地预测冲刷深度。预测的冲刷深度比其宽度更准确。敏感性分析表明，冲刷深度主要由Keulegan-Carpenter数控制，并且波高比其他独立参数对冲刷深度的影响更大。

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