Application of hybrid ANN paradigms built with nature inspired meta-heuristics for modelling soil compaction parameters

Abstract

For monitoring soil compaction in civil engineering projects, the determination of optimum moisture content (OMC) and maximum dry density (MDD) are very significant. The present method of determining the OMC and MDD through laboratory testing is a time-consuming task and requires skilled workmanship. Thus, in order to replace the conventional approach of OMC and MDD determination in the laboratory, this study presents a hybrid intelligence model of artificial neural network (ANN) and grey wolf optimizer (GWO). These two computational intelligence techniques collaborate to construct the ANN-GWO model for estimating OMC and MDD of soils. A database of 126 experimental results with six influencing factors was employed in the current work. To compare the outcomes of the developed ANN-GWO model, four additional hybrid ANNs were built utilizing particle swarm optimization, slime mould algorithm, Harris hawks optimization, and salp swarm algorithm. Additional laboratory experiments were conducted to investigate the generalization ability of the constructed ANNs. Results for the testing dataset indicate that the ANN-GWO achieves the best accurate estimation in cases of OMC (RMSE = 0.0986, PFI = 1.2569, and R2 = 0.7273) and MDD (RMSE = 0.1017, PFI = 1.2318, and R2 = 0.7147) of soils. The ANN-GWO model yields superior results than other hybrid ANNs constructed in this work. Overall, the suggested ANN-GWO can assist geotechnical engineers to estimate the OMC and MDD of soils in civil engineering works. The developed MATLAB models are also attached as a supplementary material which can readily be used to determine soil compaction parameters along with their ranges for different soil types.