Crushed Stone Grain Shapes Classification Using Convolutional Neural Networks

Currently, intelligent technologies are becoming both a topical subject for theoretical discussions and a proper tool for transforming traditional industries, including the construction industry. The construction industry intensively uses innovative methods based on intelligent algorithms of various natures. As practice shows, modern intelligent technologies based on AI surpass traditional ones in accuracy and speed of information processing. This study implements methods using convolutional neural networks, which solve an important problem in the construction industry—to classify crushed stone grains by their shape. Rapid determination of the crushed stone grain class will allow determining the content of lamellar and acicular grains, which in turn is a characteristic that affects the strength, adhesion, and filler placement. The classification algorithms were based on the ResNet50, MobileNetV3 Small, and DenseNet121 architectures. Three-dimensional images of acicular, lamellar, and cuboid grains were converted into single-channel digital tensor format. During the laboratory experiment, the proposed intelligent algorithms demonstrated high stability and efficiency. The total processing time for 200 grains, including the photo recording stage, averaged 16 min 41 s, with the accuracy reaching 92%, which is comparable to the results of manual classification by specialists. These models provide for the complete automation of crushed stone grain typing, leading to reduced labor costs and a decreased likelihood of human error.