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).
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