ANN-based swarm intelligence for predicting expansive soil swell pressure and compression strength.

ABSTRACT: This research suggests a robust integration of artificial neural networks (ANN) for predicting swell pressure and the unconfined compression strength of expansive soils (P_sUCS-ES). Four novel ANN-based models, namely ANN-PSO (i.e., particle swarm optimization), ANN-GWO (i.e., grey wolf optimization), ANN-SMA (i.e., slime mould algorithm) alongside ANN-MPA (i.e., marine predators' algorithm) were deployed to assess the P_sUCS-ES. The models were trained using the nine most influential parameters affecting P_sUCS-ES, collected from a broader range of 145 published papers. The observed results were compared with the predictions made by the ANN-based metaheuristics models. The efficacy of all these formulated models was evaluated by utilizing mean absolute error

SUBMITTER: Jalal FE 

PROVIDER: S-EPMC11199650 | biostudies-literature | 2024 Jun

REPOSITORIES: biostudies-literature