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首页> 外文期刊>Journal of Hydroinformatics >ANFIS-based approach to scour depth prediction at abutments in armored beds
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ANFIS-based approach to scour depth prediction at abutments in armored beds

机译：基于ANFIS的装甲床基台冲刷深度预测方法

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摘要

An accurate estimation of the maximum possible scour depth at bridge abutments is of paramount importance in decision-making for the safe abutment foundation depth and also for the degree of scour countermeasures to be implemented against excessive scouring. Most of the scour depth prediction formulae available in the literature have been developed based on the analysis of laboratory and field data using statistical methods such as the regression method (RM). The alternative approaches, such as artificial neural network (ANN) and adaptive neuro-fuzzy inference system (ANFIS), are generally preferred to provide better solutions in cases where the available data is incomplete or ambiguous in nature, in the present study, an attempt has, therefore, been made to develop the ANFIS model for the prediction of scour depth at the bridge abutments embedded in an armored bed and make the comparative study for the performance of ANFIS over RM and ANN in modeling the scour depth, it has been found that the ANFIS model performed best amongst all of these methods. The causative variables in raw form result in a more accurate prediction of the scour depth than that of their grouped form.
机译：准确估计桥基处最大可能冲刷深度对于安全基台基础深度以及针对过度冲刷应采取的冲刷对策程度的决策至关重要。文献中可用的大多数冲刷深度预测公式都是基于实验室和田间数据的分析，使用统计方法（例如回归方法（RM））开发的。在现有数据本质上不完整或模棱两可的情况下，通常首选使用替代方法（例如人工神经网络（ANN）和自适应神经模糊推理系统（ANFIS））来提供更好的解决方案，在本研究中，因此，已经开发出了ANFIS模型，用于预测装甲床中嵌入的桥台的冲刷深度，并对ANFIS和RM和ANN在冲刷深度建模方面的性能进行了比较研究，发现在所有这些方法中，ANFIS模型的效果最好。与分组形式相比，原始形式的因果变量可以更准确地预测冲刷深度。

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