Modelling Soil Compaction Parameters Using an Enhanced Hybrid Intelligence Paradigm of ANFIS and Improved Grey Wolf Optimiser

Abidhan Bardhan, Raushan Kumar Singh, Sufyan Ghani, Gerasimos Konstantakatos and Panagiotis G. Asteris ()
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Abidhan Bardhan: Department of Civil Engineering, National Institute of Technology, Patna 800005, India
Raushan Kumar Singh: Department of Computer Engineering and Applications, GLA University, Mathura 281406, India
Sufyan Ghani: Department of Civil Engineering, Sharda University, Greater Noida 201310, India
Gerasimos Konstantakatos: Computational Mechanics Laboratory, School of Pedagogical and Technological Education, 14121 Athens, Greece
Panagiotis G. Asteris: Computational Mechanics Laboratory, School of Pedagogical and Technological Education, 14121 Athens, Greece

Mathematics, 2023, vol. 11, issue 14, 1-23

Abstract: The criteria for measuring soil compaction parameters, such as optimum moisture content and maximum dry density, play an important role in construction projects. On construction sites, base/sub-base soils are compacted at the optimal moisture content to achieve the desirable level of compaction, generally between 95% and 98% of the maximum dry density. The present technique of determining compaction parameters in the laboratory is a time-consuming task. This study proposes an improved hybrid intelligence paradigm as an alternative tool to the laboratory method for estimating the optimum moisture content and maximum dry density of soils. For this purpose, an advanced version of the grey wolf optimiser (GWO) called improved GWO (IGWO) was integrated with an adaptive neuro-fuzzy inference system (ANFIS), which resulted in a high-performance hybrid model named ANFIS-IGWO. Overall, the results indicate that the proposed ANFIS-IGWO model achieved the most precise prediction of the optimum moisture content (degree of correlation = 0.9203 and root mean square error = 0.0635) and maximum dry density (degree of correlation = 0.9050 and root mean square error = 0.0709) of soils. The outcomes of the suggested model are noticeably superior to those attained by other hybrid ANFIS models, which are built with standard GWO, Moth-flame optimisation, slime mould algorithm, and marine predators algorithm. The results indicate that geotechnical engineers can benefit from the newly developed ANFIS-IGWO model during the design stage of civil engineering projects. The developed MATLAB models are also included for determining soil compaction parameters.

Keywords: soil compaction; adaptive neuro-fuzzy inference system; grey wolf optimiser; swarm intelligence (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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