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Employing adaptive neural fuzzy inference system model via meta-heuristic algorithms for predicting undrained shear strength

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Abstract

The undrained shear strength (USS) of soil is known as one of the most important features in structural engineering tasks, such as foundation design, rock fill and earth dam design, highway and railway design, and stability of slopes. Some empirical and theoretical procedures have been developed for estimating the USS based on soil features utilizing field tests in recent years. Almost all of these methods contain correlation assumptions which lead to inaccurate results. In addition, conventional procedures are rarely time and cost-effective. In this paper, to put down these deficiencies, novel machine learning approaches are based on the Adaptive Neural Fuzzy Inference System (ANFIS) model for predicting the Undrained shear strength of sensitive soils. Furthermore, for optimizing purposes, three meta-heuristic optimization strategies comprising Equilibrium Slime Mould Algorithm (ESOMA), School Based Optimization (SBO), and Slime Mould Algorithm (SMA) were applied. Models were trained with four input variables named liquid limit (LL), sleeve friction (SF), overburden weight (OBW), and plastic limit (PL). At the final stage, five statistical metrics (R2, RMSE, MDAE, MSE, and n20_index) were considered for evaluating the models. As a result, ANFEA obtained the highest \({R}^{2}=0.995\) and the lowest \(\text{RMSE}=62.95 (\text{Kpa})\) compared to other hybrid models.

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  1. Puyang Vocational and Technical College, Puyang, 457000, Henan, China

    Xiaoling Ding

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  1. Xiaoling Ding
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XD: Writing—original draft preparation, conceptualization, supervision, project administration.

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Ding, X. Employing adaptive neural fuzzy inference system model via meta-heuristic algorithms for predicting undrained shear strength. Multiscale and Multidiscip. Model. Exp. and Des. (2023). https://doi.org/10.1007/s41939-023-00231-3

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