Title: A mayfly optimisation method to predict load settlement of reinforced railway tracks on soft subgrade with multi-layer geogrid

Authors: M.A. Balasubramani; 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: An essential consideration in constructing this retaining structure is the deterioration of geosynthetic reinforced soil (GRS) earth structures. However, artificial intelligence can solve geotechnical problems, according to the literature. This study will show that soft computing can predict geogrid-reinforced structure deformation on railway tracks. Designing and assessing a geogrid model with poor soil railroad track material is offered. An underlying soft subgrade's effective bearing capacity is increased using the geogrid. AI predicts fine-grained soil deflection based on load cycles. The geogrid is managed using the mayfly optimisation algorithm (MOA), and it discovered that MOA prediction models function adequately. The performance of the suggested prediction models of geogrid reinforced deformations is assessed regarding the settlement, bearing capacity, deformation, and pressure of weak soil in the railway track. They were built by numerical analysis in MATLAB. The suggested technique is contrasted with traditional approaches like the cuttlefish algorithm (CFA), Harris Hawk optimisation (HHO), and artificial neural networks (ANN).

Keywords: geosynthetics; geogrid; improved subgrade; mayfly optimisation algorithm; MOA; sustainable development; cuttlefish algorithm; CFA; Harris Hawk optimisation; HHO.

DOI: 10.1504/IJHM.2023.130517

International Journal of Hydromechatronics, 2023 Vol.6 No.2, pp.159 - 176

Received: 28 Sep 2022
Accepted: 22 Feb 2023
Published online: 25 Apr 2023 *

Full-text access for editors Full-text access for subscribers Purchase this article Comment on this article