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An Artificial Intelligence Based Data-Driven Method for Forecasting Unconfined Compressive Strength of Cement Stabilized Soil by Deep Mixing Technique

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Abstract

The cost constraints imposed on construction projects specially ground improvements by the deep mixing technique (DMT) highlight the role of efficient optimization. In this study, a systematic artificial neural network (ANN)-based method to address this practical issue is presented. To achieve this goal, the mutual information method is firstly used to select the most important input parameters for training. Secondly, different ANN architectures are examined to find the network with the smallest error by training and optimizing several multi-layer perceptron networks using a grid search-based technique. The model is then generalized by employing the K-fold and Hold-out cross-validation methods. Comparing K-fold and Hold-out methods showed that K-fold has a longer computation time (approximately 4.5 times longer than the Hold-out method) but leads to higher accuracy (R2 of 0.98 as compared to R2 of 0.89 for the Hold-out method). Furthermore, the influence of each input variable on the output is examined by sensitivity analyses emphasizing the importance of the water-cement ratio and the cement content as the input parameters. Lastly, the proposed methodology is validated against a large-scale field DMT experiment. The results showed an acceptable accuracy in predicting the strength of soil after DMT with an R2 of 0.83.

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Acknowledgements

The corresponding author is grateful to the Iran’s National Elites Foundation for the financial support provided to him by way of “Dr Kazemi-Ashtiani Award”.

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  1. S. Farid F. Mojtahedi, and Adel Ahmadihosseini contributed equally to this work.

Authors and Affiliations

  1. Department of Infrastructure Engineering, University of Melbourne, Melbourne, Australia

    S. Farid F. Mojtahedi

  2. Department of Mining and Material Engineering, McGill University, Montreal, Canada

    Adel Ahmadihosseini

  3. Department of Civil Engineering, Sharif University of Technology, Tehran, Iran

    Hamed Sadeghi

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  1. S. Farid F. Mojtahedi
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Correspondence to Hamed Sadeghi.

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Appendix

Appendix

The dataset used for the development of the ANN in this study is shown in Table

Table 8 Dataset used for training and testing the ANN
Full size table

8 including all the input and output parameters.

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F. Mojtahedi, S.F., Ahmadihosseini, A. & Sadeghi, H. An Artificial Intelligence Based Data-Driven Method for Forecasting Unconfined Compressive Strength of Cement Stabilized Soil by Deep Mixing Technique. Geotech Geol Eng 41, 491–514 (2023). https://doi.org/10.1007/s10706-022-02297-1

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