Early estimation of 28-day compressive strength of mortars using regression and neural network-based models

Abstract

Concrete, the most commonly used construction material, presents a challenge in determining its compressive strength (CS) after 28 days of manufacture. This study aims to predict the compressive strength of conventionally cured (CC) mortar specimens using data from accelerated microwave-cured (MC) specimens. Two hundred and seventy-six specimens with different mixes and admixtures were divided into two groups: one half was subjected to MC and the other to CC. The CSs of the CC specimens were determined after 28 days, while the MC specimens were subjected to additional tests such as ultrasonics pulse velocity and three-point bending after microwave curing. Regression and artificial neural network models (ANN) were developed using the obtained data. While the data of the specimens subjected to MC were used as independent variables of the developed models, the compressive strength of the conventionally cured specimens (CC) was used as the dependent variable. In the models, 60% of the data were used for training, 20% for validation, and 20% for testing. The predictive capabilities of the developed models were compared using performance statistics such as root mean square error (RMSE), root mean absolute error (MAE), and Nash-Sutcliffe efficiency coefficient (NSE). As a result of the study, it was found that compared to all models, the highest performance values were obtained by the ANN-based models. Among the regression-based models, the multivariate adaptive regression splines method (MARS) obtained the highest performance values. Considering the calculated NSE values, it was determined that the ANN-based model gave similar results to the MARS model for the training and validation data sets but provided performance values approximately 25% higher than the MARS model for the test data set. In addition, the results obtained from the modeling studies showed that the CSs of concrete could be predicted very accurately and well in advance.