Author Identifier

Sanjay Kumar Shukla

ORCID : 0000-0002-4685-5560

Document Type

Journal Article

Publication Title

Geotextiles and Geomembranes

Publisher

Elsevier

School

School of Engineering

RAS ID

35979

Funders

Edith Cowan University - Open Access Support Scheme 2021

Edith Cowan University

Higher Education Commission, Pakistan

Comments

Raja, M. N. A., & Shukla, S. K. (2021). Predicting the settlement of geosynthetic-reinforced soil foundations using evolutionary artificial intelligence technique. Geotextiles and Geomembranes, 49(5), 1280-1293. https://doi.org/10.1016/j.geotexmem.2021.04.007

Abstract

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 well-established 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.

DOI

10.1016/j.geotexmem.2021.04.007

Creative Commons License

Creative Commons Attribution 4.0 License
This work is licensed under a Creative Commons Attribution 4.0 License.

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