Accuracy assessment of extreme learning machine in predicting soil compression coefficient

Hai-Bang Ly; Panagiotis G. Asteris; Thai Binh Pham

doi:10.15625/0866-7187/42/3/14999

Author affiliations

Authors

Hai-Bang Ly University of Transport Technology, Hanoi 100000, Vietnam
Panagiotis G. Asteris Computational Mechanics Laboratory, School of Pedagogical and Technological Education, Heraklion, GR 14121, Athens, Greece
Thai Binh Pham University of Transport Technology, Hanoi 100000, Vietnam

DOI:

https://doi.org/10.15625/0866-7187/42/3/14999

Keywords:

Compression coefficient, extreme machine learning, Monte Carlo simulations

Abstract

The compression coefficient (Cc) is an important soil mechanical parameter that represents soil compressibility in the process of consolidation. In this study, a machine learning derived model, namely extreme learning algorithm (ELM), was used to predict the Cc of soil. A total of 189 experimental results were used and randomly divided to construct the training and testing parts for the development and validation of ELM. Monte Carlo approach was applied to take into account the random sampling of samples constituting the training dataset. A number of 13 input parameters reflecting the experiment were used as the input variables to predict the output Cc. Several statistical criteria, such as mean absolute error (MAE), root mean square error (RMSE), correlation coefficient (R) and the Monte Carlo convergence estimator were used to assess the performance of ELM in predicting the Cc of soil. The results showed that ELM had a strong capacity to predict the Cc of soil, with the R value > 0.95. The convergence of results, as well as the capability of ELM were fully investigated to understand the advantage of using ELM as a predictor.

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