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

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Abstract

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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Authors and Affiliations

  1. Department of Civil Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran

    Ahmad Sharafati

  2. Department of Structural Mechanics & Hydraulics Engineering, University of Granada, Granada, 18001, Spain

    Masoud Haghbin

  3. EIT. Schnabel Engineering, Chadds Ford, PA, 19317, USA

    Mohammadamin Torabi

  4. New Era and Development in Civil Engineering Research Group, Scientific Research Center, Al-Ayen University, Thi-Qar, 64001, Iraq

    Zaher Mundher Yaseen

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  1. Ahmad Sharafati
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  2. Masoud Haghbin
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  3. Mohammadamin Torabi
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  4. Zaher Mundher Yaseen
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Correspondence to Ahmad Sharafati.

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Sharafati, A., Haghbin, M., Torabi, M. et al. Assessment of novel nature-inspired fuzzy models for predicting long contraction scouring and related uncertainties. Front. Struct. Civ. Eng. 15, 665–681 (2021). https://doi.org/10.1007/s11709-021-0713-0

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  • DOI: https://doi.org/10.1007/s11709-021-0713-0

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