Eur. Phys. J. Plus (2020) 135: 682
https://doi.org/10.1140/epjp/s13360-020-00703-2

Regular Article

Machine learning approaches for estimation of compressive strength of concrete

¹ Faculty of Civil Engineering and Architecture, Josip Juraj Strossmayer University of Osijek, Vladimira Preloga 3, 31000, Osijek, Croatia
² Faculty of Electrical Engineering, Computer Science and Information Technology Osijek, Kneza Trpimira 2b, 31000, Osijek, Croatia
³ Division of Construction Engineering and Management, Purdue University, 47907, West Lafayette, IN, USA
⁴ College of Hydraulic Science and Engineering, Yangzhou University, 225127, Yangzhou, China
⁵ State Key Laboratory of Hydrology-Water Resources and Hydraulic Engineering, Nanjing Hydraulic Research Institute, 210029, Nanjing, China

^d slzhu@nhri.cn

Received: 17 June 2020
Accepted: 19 August 2020
Published online: 29 August 2020

Abstract

Estimation of compressive strength of rubberized concrete is important for engineering safety. In this study, measured data (the compressive strength of rubberized concrete and its impacting factors) were collected by literature review (457 samples). In order to accurately predict the compressive strength of rubberized concrete, four machine learning models [artificial neural network (ANN), k-nearest neighbor (KNN), regression trees (RT), and random forests (RF)] were developed and compared to estimate the compressive strength of rubberized concrete, and the modeling results were compared with two traditional expressions. The model performance was evaluated using three performance indicators: the Nash–Sutcliffe efficiency coefficient (NSC), the root-mean-squared error (RMSE), and the mean absolute error (MAE). The results showed that the RT model performs the best, followed by the ANN and RF in the model training phase. In the model testing phase, the ANN model performs the best, followed by the RT, RF, and KNN. The overall results indicated that the ANN model performs the best, followed by RT and RF, and the KNN model performs the worst. The ANN and RT models outperformed the two traditional expressions. The tree-based models (RT and RF) and KNN model may not be applicative to estimate the compressive strength of rubberized concrete due to the generally poor performances in the model testing phase compared with that in the model training phase. The results showed that the traditional ANN model is sufficient for the accurate estimation of the compressive strength of rubberized concrete when the model is properly trained. The results in the present research can provide reference for the prediction of the compressive strength of rubberized concrete, which will benefit engineering management and safety as well.