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Soil Unconfined Compressive Strength Prediction Using Random Forest (RF) Machine Learning Model

Author(s):

Medium: journal article
Language(s): en 
Published in: The Open Construction and Building Technology Journal, , n. 1, v. 14
Page(s): 278-285
DOI: 10.2174/1874836802014010278
Abstract:

Aims:

Understanding the mechanical performance and applicability of soils is crucial in geotechnical engineering applications. This study investigated the possibility of application of the Random Forest (RF) algorithm – a popular machine learning method to predict the soil unconfined compressive strength (UCS), which is one of the most important mechanical properties of soils.

Methods:

A total number of 118 samples collected and their tests derived from the laboratorial experiments carried out under the Long Phu 1 power plant project, Vietnam. Data used for modeling includes clay content, moisture content, specific gravity, void ratio, liquid limit and plastic limit as input variables, whereas the target is the UCS. Several assessment criteria were used for evaluating the RF model, namely the correlation coefficient (R), root mean squared error (RMSE) and mean absolute error (MAE).

Results:

The results showed that RF exhibited a strong capability to predict the UCS, with the R value of 0.914 and 0.848 for the training and testing datasets, respectively. Finally, a sensitivity analysis was conducted to reveal the importance of input parameters to the prediction of UCS using RF. The specific gravity was found as the most affecting variable, following by clay content, liquid limit, plastic limit, moisture content and void ratio.

Conclusion:

This study might help in the accurate and quick prediction of the UCS for practice purpose.

Copyright: © 2020 Hai-Bang Ly, Binh Thai Pham
License:

This creative work has been published under the Creative Commons Attribution 4.0 International (CC-BY 4.0) license which allows copying, and redistribution as well as adaptation of the original work provided appropriate credit is given to the original author and the conditions of the license are met.

  • About this
    data sheet
  • Reference-ID
    10443627
  • Published on:
    05/10/2020
  • Last updated on:
    02/06/2021