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.
Highlights
In the current construction practice, geosynthetic-reinforced soils are widely used to support shallow foundations
The review of the published literature shows that the use of evolutionary computation methods based on metaheuristic algorithm seems to be the robust technique for improving the efficiency of artificial intelligence (AI) models specially artificial neural network (ANN) (e.g., genetic algorithm (GA); artificial bee col ony optimisation algorithm (ABCO); particle swarm optimisation (PSO); grey wolf optimisation (GWO); whale optimisation (WO); flower polli nation algorithm (FPA) etc.)
The settlement estimation of geosynthetic-reinforced soil foundation under service load is of paramount importance for practicing geotechnical engineers
Summary
In the current construction practice, geosynthetic-reinforced soils are widely used to support shallow foundations. Lee (1975) conducted an experimental study to investigate the load-settlement behaviour of footing resting on soil bed reinforced with aluminium strips. The review of the published literature shows that the use of evolutionary computation methods based on metaheuristic algorithm seems to be the robust technique for improving the efficiency of AI models specially ANNs (e.g., genetic algorithm (GA); artificial bee col ony optimisation algorithm (ABCO); particle swarm optimisation (PSO); grey wolf optimisation (GWO); whale optimisation (WO); flower polli nation algorithm (FPA) etc.) Amongst these methods, GWO algorithm is a recently developed metaheuristic, and has shown a promising ability in finding the optimum solutions to constrained as well as unconstrained engineering problems (Guha et al, 2016; Mirjalili et al, 2014; Moayedi et al, 2019; Tikhamarine et al, 2020). This will prove helpful in saving time and cost (monetary and compu tational) associated with performing model footing load tests and nu merical simulations
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