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Prediction of soil degree of compaction based on machine learning: a case study of two fine-grained soils

Yuling Ran, Wei Bai, Lingwei Kong, Henghui Fan, Xiujuan Yang, Xuemei Li

Engineering Computations

ISSN: 0264-4401

Article publication date: 24 November 2023

Issue publication date: 4 March 2024

132

Abstract

Purpose

The purpose of this paper is to develop an appropriate machine learning model for predicting soil compaction degree while also examining the contribution rates of three influential factors: moisture content, electrical conductivity and temperature, towards the prediction of soil compaction degree.

Design/methodology/approach

Taking fine-grained soil A and B as the research object, this paper utilized the laboratory test data, including compaction parameter (moisture content), electrical parameter (electrical conductivity) and temperature, to predict soil degree of compaction based on five types of commonly used machine learning models (19 models in total). According to the prediction results, these models were preliminarily compared and further evaluated.

Findings

The Gaussian process regression model has a good effect on the prediction of degree of compaction of the two kinds of soils: the error rates of the prediction of degree of compaction for fine-grained soil A and B are within 6 and 8%, respectively. As per the order, the contribution rates manifest as: moisture content > electrical conductivity >> temperature.

Originality/value

By using moisture content, electrical conductivity, temperature to predict the compaction degree directly, the predicted value of the compaction degree can be obtained with higher accuracy and the detection efficiency of the compaction degree can be improved.

Keywords

Acknowledgements

This work was supported by the Hubei Provincial Natural Science Foundation of China (Grant No. 2023AFB835) and the National Natural Science Foundation of China (Grant No. 41772339).

Citation

Ran, Y., Bai, W., Kong, L., Fan, H., Yang, X. and Li, X. (2024), "Prediction of soil degree of compaction based on machine learning: a case study of two fine-grained soils", Engineering Computations, Vol. 41 No. 1, pp. 46-67. https://doi.org/10.1108/EC-06-2023-0304

Publisher

:

Emerald Publishing Limited

Copyright © 2023, Emerald Publishing Limited

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