Predicting the settlement of geosynthetic-reinforced soil foundations using evolutionary artificial intelligence technique

• Published in: Geotextiles and geomembranes Vol. 49; no. 5; pp. 1280 - 1293
• Main Authors: Raja, Muhammad Nouman Amjad, Shukla, Sanjay Kumar
• Format: Journal Article
• Language: English
• Published: Essex Elsevier Ltd 01.10.2021; Elsevier BV
• Subjects: ANN-GWO; Artificial intelligence; Artificial neural networks; Evolutionary algorithms; Finite element analysis; Finite element method; Finite element simulations; Foundation settlement; Geosynthetics; Geotechnical engineering; Hybrid model; Mathematical models; Optimization; Predictive modelling; Preliminary designs; Reinforced soil foundation; Reinforced soils; Reliability analysis; Robustness (mathematics); Sensitivity analysis; Settlement; Settlement analysis; Soil settlement; Soils; Three dimensional models; Geosynthetics; Settlement; Finite element simulations; Reinforced soil foundation; Predictive modelling; Hybrid model; ANN-GWO; Artificial intelligence
• ISSN: 0266-1144; 1879-3584
• DOI: 10.1016/j.geotexmem.2021.04.007

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. [Display omitted] •Predicting the settlement of geosynthetic-reinforced soil foundations (GRSF).•Development of hybrid grey wolf optimised artificial neural network (ANN-GWO).•Validating the developed model through rigorous statistical testing, sensitivity and robustness analysis.•Derivation of trackable mathematical relationship for settlement prediction of GRSF.