Determination of concrete compressive strength from surface images with the integration of CNN and SVR methods

Abstract

In this study, a new method has been developed using Convolutional Neural Networks (CNN) and Support Vector Regression (SVR) integration to determine the compressive strength of concrete, which is the most common structural material used in the construction sector. The developed CNN model (MPaConvNet) extracts important features from concrete surface images. These extracted features are then used to predict the compressive strength of concrete using the SVR algorithm. The dataset used in the study consists of 1453 concrete surface images obtained from 203 core samples collected from 115 real buildings. The proposed method predicted the compressive strength of concrete with the values of 99.59%, 0.0209, 0.6836, 0.4673, and 1.0000 according to the R2, MAPE, RMSE, MSE, and a-20 metrics, respectively. Additionally, in the study, the regions focused on by the proposed CNN architecture in concrete surface images were detected using the Gradient-weighted Class Activation Mapping algorithm (Grad-CAM).