Abstract
Estimating soil compaction parameters is an essential point when seeking safe and economic dams, bridges abutments, highways embankments, retaining walls, and nuclear waste disposal; however, the Proctor Test employed in this forecasting is costly and time-consuming. The current study, therefore, aims at elaborating a new alternative model for predicting the compaction parameters based on eleven new progressive machine learning methods to overcome these limitations. The modeling phase was performed using a database of 147 samples collected from different studies. Additionally, six relevant factors were selected in the input layer based on the literature recommendations. Furthermore, the relevant inputs have been modeled using machine learning methods; their performance was assessed through six performance measures and the K-fold cross-validation approach. The comparative study proved the effectiveness of the RF model, which displayed the highest performance in predicting soil compaction parameters. This elaborated model provided the optimal prediction, i.e., the closest to the experimental values compared to other models and formulae proposed in the literature. Finally, a reliable and easy-to-use graphical interface was generated in the current study dubbed “ComPara2021.” This latter will be very helpful for researchers and civil engineers when estimating compaction parameters with the advantage of saving time and money.
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Acknowledgements
The authors send their deepest gratitude to the Laboratory of Civil Engineering Materials and Environment Laboratory (LMGSE) of the National Polytechnic School of Algiers, for providing all the necessary materials used in conducting the study. We would like to thank Pr. Alexandru-Ionut Petrisor and Pr. Ratiba Mitiche Kettab for giving us a supported hand in develo** our studies. We would like to thank Ph.D student Djaidja Asma for their valuable support to improve the quality of the paper, especially for the contribution in the linguistic parts.
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Dr. Benbouras Mohammed Amin acquired methodology, software, data curation, and writing and contributed to the investigation. Ms. Lefilef Lina provided resources and contributed to writing—review and editing, and data curation.
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Appendix 1 The scatter plots between target and output soil compaction value by the advanced machine learning models
Appendix 1 The scatter plots between target and output soil compaction value by the advanced machine learning models
Scatter plots between target and output value by the SVR model
Scatter plots between target and output value by the DNN model
Scatter plots between target and output value by the ELM model
Scatter plots between target and output value by the GP model
Scatter plots between target and output value by KRidge model
Scatter plots between target and output value by lasso model
Scatter plots between target and output value by the PLS model
Scatter plots between target and output value by the RF model
Scatter plots between target and output value by the Ridge model
Scatter plots between target and output value by the STEP model
Scatter plots between target and output value by the LS model
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Benbouras, M.A., Lefilef, L. Progressive Machine Learning Approaches for Predicting the Soil Compaction Parameters. Transp. Infrastruct. Geotech. 10, 211–238 (2023). https://doi.org/10.1007/s40515-021-00212-4
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DOI: https://doi.org/10.1007/s40515-021-00212-4