Development of a machine learning model for on-site evaluation of concrete compressive strength by SonReb

Abstract

Coring or destructive testing is typically the default choice for the evaluation of concrete compressive strength in reinforced concrete (RC) structures. However, it can be impractical and/or not representative of all areas of interest in a structure. While various non-destructive test (NDT) methods can be correlated to concrete strength, the accuracy of any single NDT used for this purpose is generally low. The SonReb method, which combines ultrasonic pulse velocity readings and rebound number, has been shown to have improved accuracy over single test methods. However, empirical SonReb equations, calibrated to specific datasets using regression analysis, cannot necessarily be applied to concrete from other sources without introducing significant errors. This study presents a practical machine learning (ML) model for on-site concrete strength prediction. A large database was created from available literature along with new experimental test data. Three different ML models based on an adaptive neuro-fuzzy inference system (ANFIS) were developed along with a graphical user interface application to facilitate its use in the field. In addition to the ML models, linear and non-linear regression analyses were also conducted and compared with existing equations in the literature. The accuracy of each model was subsequently validated against core samples extracted from a reinforced concrete slab. The results show that the proposed ML model and non-linear regression provided the most reliable predictions of concrete strength of the validation specimen with a mean absolute error of less than 10 % compared with twelve core samples. The findings suggest that ML can be a potential tool to evaluate the in-place compressive strength of RC structures in combination with simple NDT methods.