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A Comparative Analysis of Hybrid Computational Models Constructed with Swarm Intelligence Algorithms for Estimating Soil Compression Index

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Abstract

The determination of the compression index (Cc) of clay through oedometer tests is time-consuming and expensive. To replace the practice of conducting laboratory oedometer tests, this study presents a comparative analysis of hybrid machine learning models for estimating the soil Cc based on actual laboratory test data. Ten swarm intelligence algorithms, namely particle swarm optimization, ant colony optimization, artificial bee colony, grey wolf optimizer, moth flame optimizer, whale optimization algorithm, salp swarm algorithm, Harris hawks optimization, slime mould algorithm, and marine predator algorithm, were used to optimize the learning parameters of an artificial neural network (ANN) and adaptive neuro-fuzzy inference system (ANFIS). Subsequently, 20 hybrid ANN and ANFIS models were constructed to obtain the best prediction model. A sum of 700 oedometer test data was acquired from an Indian Railways project to construct and validate the hybrid models. Besides, 30 new oedometer experiments were performed for external validation of the developed hybrid models. Experimental outcomes show that the proposed ANFIS and PSO hybrid model (ANFIS-PSO) attained the most accurate prediction of soil Cc, which is much superior to the developed hybrid ANN and ANFIS models. Based on the experimental results, the proposed ANFIS-PSO model demonstrates high potential as a robust alternative to the actual oedometer test to assist geotechnical engineers in the introductory stage of civil engineering projects.

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Acknowledgements

The authors are very thankful to the Dedicated Freight Corridor of India Limited (DFCCIL) New Delhi, India and officials of Larsen and Toubro Construction, DFCCIL CTP-3(R) Project Site, Ahmedabad, India to provide experimental data and their kind support during the course of this study.

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Authors and Affiliations

  1. Department of Civil Engineering, National Institute of Technology (NIT) Patna, Patna, Bihar, 800005, India

    Abidhan Bardhan & Pijush Samui

  2. Discipline of Civil and Infrastructure Engineering, School of Engineering, Royal Melbourne Institute of Technology (RMIT), GPO Box 2476, Melbourne, VIC, 3001, Australia

    Navid Kardani

  3. Department of Civil Engineering, Abu Dhabi University, Al Ain, United Arab Emirates

    Abdel Kareem Alzo’ubi

  4. Faculty of Engineering and Information Technology, University of Technology Sydney, Sydney, Australia

    Amir H. Gandomi

  5. Department of Geological Engineering, Hacettepe University, Beytepe, 06800, Ankara, Turkey

    Candan Gokceoglu

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AB: Main author, conceptualization, literature review, model development, statistical analysis, detailing, overall analysis, manuscript writing, and finalization; NK: Manuscript writing, software and analysis; AKA: Review and editing; PS: Guidance; AHG: Detailed review and editing; CG: Detailed review and editing.

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Bardhan, A., Kardani, N., Alzo’ubi, A.K. et al. A Comparative Analysis of Hybrid Computational Models Constructed with Swarm Intelligence Algorithms for Estimating Soil Compression Index. Arch Computat Methods Eng 29, 4735–4773 (2022). https://doi.org/10.1007/s11831-022-09748-1

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