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Bio-inspired Predictive Models Development for Strength Characterization of Cement Deep-Mixed Plastic Soils

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Abstract

This paper utilizes various artificial intelligence models to predict the experimental results of the deep-mixing technology for ground improvement and stabilization. A total of 192 unconfined compression strength laboratory experiments were conducted on specimens taken from Khuzestan, Iran, to compare the strength of the soil before and after the treatment with cement. In this research, 144 sets of experimental data, constituting 75% of the total, were used for training, while 48 sets, equivalent to 25% of the experimental data, were utilized for both testing. Different artificial intelligence methods including artificial neural networks, hybrid artificial bee colony-artificial neural networks, combinational group modeling of data handling, and gene expression programming were used. To evaluate the performance of each method, mean squared error, root mean squared error, mean absolute percentage error, mean absolute error, linear correlation coefficient, and coefficient of determination was calculated for each method. Based on the performance analysis, the hybrid artificial bee colony-artificial neural network algorithm outperformed other methods with an R2 calculated as 0.9969 and 0.9952, respectively in training and testing. The R2 values during training for ANN, GMDH, and GEP are 0.993, 0.983, and 0.96, respectively. Likewise, during testing, the R2 values for ANN, GMDH, and GEP are 0.992, 0.978, and 0.953, respectively. The results demonstrated significant agreement between artificial intelligence predictive models and experimental data. Furthermore, this paper provides robust and cost-effective models with a “closed-form solution” for predicting the strength of stabilized soils by the deep mixing technique. The closed-form equation presented in this study, which is derived from the group method of data handling combinatorial algorithm and gene expression programming models, is more intuitive for engineers to apply.

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Data Availability

The datasets generated during and/or analyzed during the current study are not publicly available due to its huge amount of data but are available from the corresponding author on reasonable request.

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Funding

The authors declare that no funds, grants, or other support were received during the preparation of this manuscript.

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Authors and Affiliations

  1. Infrastructure Engineering Department, University of Melbourne, Parkville VIC 3010, Melbourne, Australia

    Farid Fazel Mojtahedi

  2. Mining Engineering Department, McGill University, 845 Rue Sherbrooke O, Montréal, QC, H3A 0G4, Canada

    Adel Ahmadihosseini

  3. Department of Civil Engineering, Faculty of Engineering, Ferdowsi University of Mashhad (FUM), Azadi Square, Mashhad, Iran

    Danial Rezazadeh Eidgahee

  4. Schulich School of Engineering, University of Calgary, 2500 University Dr NW, Calgary, Canada

    Milad Rezaee

  5. DMT GmbH & Co. KG, Am TÜV 1, 45307, Essen, Germany

    Giovanni Spagnoli

Authors
  1. Farid Fazel Mojtahedi
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  2. Adel Ahmadihosseini
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  3. Danial Rezazadeh Eidgahee
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  4. Milad Rezaee
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  5. Giovanni Spagnoli
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Contributions

All authors contributed to the study conception and design. Data collection and analysis were performed by FFM, AA, DRE, MR and GS. The first draft of the manuscript was written by FFM and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.

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Correspondence to Giovanni Spagnoli.

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Mojtahedi, F.F., Ahmadihosseini, A., Eidgahee, D.R. et al. Bio-inspired Predictive Models Development for Strength Characterization of Cement Deep-Mixed Plastic Soils. Int. J. of Geosynth. and Ground Eng. 10, 9 (2024). https://doi.org/10.1007/s40891-023-00508-0

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