Prediction of Compaction and Strength Properties of Amended Soil Using Machine Learning

Woubishet Zewdu Taffese*, Kassahun Admassu Abegaz

*Corresponding author for this work

Research output: Contribution to journalArticleScientificpeer-review

2 Citations (Scopus)
14 Downloads (Pure)


In the current work, a systematic approach is exercised to monitor amended soil reliability for a housing development program to holistically understand the targeted material mixture and the building input derived, focusing on the three governing parameters: (i) optimum moisture content (OMC), (ii) maximum dry density (MDD), and (iii) unconfined compressive strength (UCS). It is in essence the selection of machine learning algorithms that could optimally show the true relation of these factors in the best possible way. Thus, among the machine learning approaches, the optimizable ensemble and artificial neural networks were focused on. The data sources were those compiled from wide-ranging literature sources distributed over the five continents and twelve countries of origin. After a rigorous manipulation, synthesis, and results analyses, it was found that the selected algorithms performed well to better approximate OMC and UCS, whereas that of the MDD result falls short of the established threshold of the set limits referring to the MSE statistical performance evaluation metrics.

Original languageEnglish
Article number613
Number of pages15
Issue number5
Publication statusPublished - May 2022
MoE publication typeA1 Journal article-refereed


  • amended soil
  • machine learning
  • maximum dry density
  • optimum moisture content
  • unconfined compressive strength


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