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《结构与土木工程前沿(英文)》 >> 2021年 第15卷 第3期 doi: 10.1007/s11709-021-0713-0

Assessment of novel nature-inspired fuzzy models for predicting long contraction scouring and related uncertainties

收稿日期: 2020-04-01 修回日期: None 录用日期: 2021-07-14 发布日期: 2021-06-15

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摘要

The scouring phenomenon is one of the major problems experienced in hydraulic engineering. In this study, an adaptive neuro-fuzzy inference system is hybridized with several evolutionary approaches, including the ant colony optimization, genetic algorithm, teaching-learning-based optimization, biogeographical-based optimization, and invasive weed optimization for estimating the long contraction scour depth. The proposed hybrid models are built using non-dimensional information collected from previous studies. The proposed hybrid intelligent models are evaluated using several statistical performance metrics and graphical presentations. Besides, the uncertainty of models, variables, and data are inspected. Based on the achieved modeling results, adaptive neuro-fuzzy inference system–biogeographic based optimization (ANFIS-BBO) provides superior prediction accuracy compared to others, with a maximum correlation coefficient (Rtest = 0.923) and minimum root mean square error value (RMSEtest = 0.0193). Thus, the proposed ANFIS-BBO is a capable cost-effective method for predicting long contraction scouring, thus, contributing to the base knowledge of hydraulic structure sustainability.

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