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  Volume 1, Number 2, December 2020, pages 155-163
DOI: https://doi.org/10.12989/mca.2020.1.2.155
 
open access

Compressive strength prediction of high-strength concrete using support vector regression
Hai-Bang Ly and Binh Thai Pham

 
Abstract
    Determination of compressive strength of concrete at early testing age is vital in many civil engineering applications. The strength at 7 or 14 days allows engineers to have confidence in the target strength and make a decision in case of unsuspected situations. In this study, the possibility to estimate the early compressive strength of concrete by a machine learning algorithm, namely the support vector regression (SVR), was investigated. To this aim, a database containing 324 data points was gathered from the available literature and use to develop the ML model. For the assessment of the accuracy, common statistical measurements, such as the Pearson correlation coefficient (R) and root mean square error (RMSE) were used. The results showed that the SVR model could successfully model the early compressive strength of concrete with R=0.92386 and RMSE=5.5089 MPa. The sensitivity analysis on the factors exhibiting a positive or negative effect on the early strength of concrete was conducted. The cement content was shown to have the most influential effect on the early development of concrete compressive strength.
 
Key Words
    early compressive strength; concrete; support vector regression; machine learning
 
Address
Hai-Bang Ly and Binh Thai Pham:University of Transport Technology, Hanoi 100000, Vietnam
 

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