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

Author

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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)

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.

Suggested Citation

  • Abidhan Bardhan & Raushan Kumar Singh & Sufyan Ghani & Gerasimos Konstantakatos & Panagiotis G. Asteris, 2023. "Modelling Soil Compaction Parameters Using an Enhanced Hybrid Intelligence Paradigm of ANFIS and Improved Grey Wolf Optimiser," Mathematics, MDPI, vol. 11(14), pages 1-23, July.
    • Handle: RePEc:gam:jmathe:v:11:y:2023:i:14:p:3064-:d:1191591
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    References listed on IDEAS

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    1. Mohammed A. A. Al-qaness & Ahmed A. Ewees & Hong Fan & Laith Abualigah & Mohamed Abd Elaziz, 2020. "Marine Predators Algorithm for Forecasting Confirmed Cases of COVID-19 in Italy, USA, Iran and Korea," IJERPH, MDPI, vol. 17(10), pages 1-14, May.
    2. Sina Paryani & Aminreza Neshat & Saman Javadi & Biswajeet Pradhan, 2020. "Comparative performance of new hybrid ANFIS models in landslide susceptibility mapping," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 103(2), pages 1961-1988, September.
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    Cited by:

    1. Oludamilare Bode Adewuyi & Senthil Krishnamurthy, 2024. "Performance Analysis for Predictive Voltage Stability Monitoring Using Enhanced Adaptive Neuro-Fuzzy Expert System," Mathematics, MDPI, vol. 12(19), pages 1-16, September.

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