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Predicting the Maximum Dry Density of Soil by Using the Individual and Hybrid Framework of the Decision Tree

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Abstract

The density of embankment fill soil material is a crucial factor determined by the maximum dry density (MDD) in transport construction projects. Traditionally, the modified proctor test is conducted in laboratories to determine this parameter. However, this paper presents a novel method that utilizes decision tree (DT) analysis to forecast the MDD of soil stabilizing mixtures. The DT method is employed to create accurate and comprehensive models that establish relationships between the MDD of stabilized soil and various natural soil properties, including particle size distribution, plasticity, linear shrinkage, and the type and quantity of stabilizing additives. These models are trained, validated, and tested with an experimental dataset of soil types obtained from previously published stabilization test results. The study concludes that DT is a viable alternative approach for predicting MDD based on input parameters. To enhance the accuracy of the DT model in predicting MDD, two meta-heuristic algorithms, namely Escaping Bird Search optimization and Runge–Kutta optimization, are integrated. This integration led to the development of two hybrid models, DTEB and DTRU. The DTEB model achieves impressive coefficient correlation (R2) values of 0.9950, 0.9863, and 0.9908 for the training, validation, and testing data, respectively. Additionally, DTEB demonstrates the most favourable root-mean-square error of 16.60. Overall, the DTEB model demonstrates acceptable predictive ability and superior generalization ability compared to the DT and DTRU models developed in this study.

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  1. Department of Civil Engineering, School of Engineering, Zhengzhou Technology and Business University, Zhengzhou, 451400, Henan Province, China

    Zheng Weifang

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  1. Zheng Weifang
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Correspondence to Zheng Weifang.

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Weifang, Z. Predicting the Maximum Dry Density of Soil by Using the Individual and Hybrid Framework of the Decision Tree. Indian Geotech J (2023). https://doi.org/10.1007/s40098-023-00827-z

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