Title: Multilayer geocell-reinforced soils using mayfly optimisation predicts circular foundation load settlement

Authors: S. Jeyanthi; R. Venkatakrishnaiah; K.V.B. Raju

Addresses: Department of Civil Engineering, Bharath Institute of Higher Education and Research, Tamil Nadu, India ' Department of Civil Engineering, Bharath Institute of Higher Education and Research, Tamil Nadu, India ' Department of Civil Engineering, Bharath Institute of Higher Education and Research, Tamil Nadu, India

Abstract: The pressure-settlement in soil layers reinforced by geocells may be calculated using the mayfly optimisation algorithm (MOA) approach presented in this research. The suggested MOA method specifically and successfully estimates the geocell pocket shape, soil and geocell reinforcing stiffness impacts, compaction-induced stresses, and soil strength and strain compatibility. The method may be used with simple, presented non-dimensional charts and analytically. Plate load results on geocell-reinforced foundation beds have been used to validate the proposed MOA model. High accuracy and consistency were found when the ANN, ANN-EHO, JSA, and suggested MOA methods were compared, particularly at predicted and actual resolution levels. The mayfly optimisation algorithm is more likely to discover a global optimal than the other approaches, though it might not be the fastest among them. The mayfly optimisation algorithm's convergence behaviour is unique because it frequently achieves the best general solution in the initial rounds. To understand geocell-reinforced structures, parametric sensitivity was investigated. This study found that increasing geocell layers, secant modulus, and soil modulus number increased bearing pressure and decreased settlement.

Keywords: mayfly optimisation algorithm; MOA; geosynthetic; geocell layers; bearing pressure; settlement; jellyfish algorithm; elephant herding optimisation; EHO; artificial neural network; ANN.

DOI: 10.1504/IJHM.2024.135984

International Journal of Hydromechatronics, 2024 Vol.7 No.1, pp.31 - 48

Received: 24 Feb 2023
Accepted: 05 Jul 2023
Published online: 11 Jan 2024 *

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