Prediction of water absorption of recycled coarse aggregate based on deep learning image segmentation

Abstract

To quickly measure the water absorption (WA) of Recycled Coarse Aggregates (RCA), we utilize a detection platform designed for RCA to collect two-dimensional images. Utilizing the RCA-net network, we segment the areas of the mortar and aggregate on the RCA surface. Segmentations allow us to extract critical parameters for characterizing the quality of RCA, the proportion of mortar area (PMA). Subsequently, we construct three regression functions between PMA and WA. The experimental results demonstrate that our proposed segmentation method effectively separates both adhered particles of RCA and distinct areas of mortar and aggregate on RCA surfaces. Next, sprinkling water on RCA surfaces can enhance the accuracy of the segmentation. Notably, within particle size ranges of 5–10 mm, 10–20 mm, and 20–31.5 mm, we all observed a significant linear relationship between PMA and WA. We used those linear relationships and the equivalent mass of RCA detected by the image method in each particle size range to construct the prediction model of water absorption. According to the validation result of 24 groups RCA, this model’s maximum relative error of RCA water absorption predicted value was 10.6 %. The detection time of this method is short, and the detection time of 2 kg RCA is 3.8 min, with an average computation time per image of merely 0.659 s. This efficiency fulfills the requirements for real-time industrial inspection.