Journal
GEOTEXTILES AND GEOMEMBRANES
Volume 49, Issue 5, Pages 1280-1293Publisher
ELSEVIER SCI LTD
DOI: 10.1016/j.geotexmem.2021.04.007
Keywords
Geosynthetics; Reinforced soil foundation; Settlement; Finite element simulations; Predictive modelling; Artificial intelligence; ANN-GWO; Hybrid model
Funding
- Higher Education Commission (HEC), Pakistan
- Edith Cowan University (ECU), Australia
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This study proposes a new hybrid technique, the ANN-GWO model, for predicting settlement of geosynthetic-reinforced soil foundations. The model's predictive power was evaluated through numerical simulations, statistical indices, and sensitivity analysis. Results indicate that the model can reliably and intelligently estimate maximum settlement of GRSF.
In order to ensure safe and sustainable design of geosynthetic-reinforced soil foundation (GRSF), settlement prediction is a challenging task for practising civil/geotechnical engineers. In this paper, a new hybrid technique for predicting the settlement of GRSF has been proposed based on the combination of evolutionary algorithm, that is, grey-wolf optimisation (GWO) and artificial neural network (ANN), abbreviated as ANN-GWO model. For this purpose, the reliable pertinent data were generated through numerical simulations conducted on validated large-scale 3-D finite element model. The predictive power of the model was assessed using various wellestablished statistical indices, and also validated against several independent scientific studies as reported in literature. Furthermore, the sensitivity analysis was conducted to examine the robustness and reliability of the model. The results as obtained have indicated that the developed hybrid ANN-GWO model can estimate the maximum settlement of GRSF under service loads in a reliable and intelligent way, and thus, can be deployed as a predictive tool for the preliminary design of GRSF. Finally, the model was translated into functional relationship which can be executed without the need of any expensive computer-based program.
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