Automatic identification and characteristics analysis of crack tips in rocks with prefabricated defects based on deep learning methods

In complex geological environments, the morphology, orientation and distribution characteristics of cracks in the rock directly affect the stability assessment for rock masses and engineering safety decisions. However, the traditional manual interpretation method is inefficient and influenced by subjective factors, which makes it tough to fulfill the requirements for high-precision and automated detection. Especially in the rock specimen analysis of prefabricated multi-angle cracks, image quality and algorithm adaptability have emerged as the critical bottlenecks restricting the identification accuracy. For this reason, it is pressingly essential to realize high-precision and automatic identification in the crack tip of the rock. Firstly, in this study, SCB semi-circular disk specimens are exposed to three-point bending loading, which is sandstone with prefabricated cracks at 0°, 15°, 30°, 45° and 60°. The microsecond-level expansion process of multi-directional cracks is monitored by utilizing an ultrafast camera in the rock specimens. Secondly, three equalization methods are applied to the collected crack images of the rock specimens, including HE, AHE, and CLAHE, to enhance the accuracy of identifying cracks in the rock specimens. And the preprocessed crack images of the rock specimens are compared, which reveals the CLAHE method possesses the optimum preprocessing effect. Based on this, pixel-level annotations are performed on the pretreated crack images, and a dataset is established about cracks in the rock specimen at five different angles. The Deeplabv3 network and the U-Net network are adopted to build cracks recognition models of the rock specimen to predict and identify the crack tips on the rock. The final results demonstrate that the recognition accuracy of the U-net model is able to reach up to 99.4%, the precision is capable of amount to 97.3%, and the recall rate can attain to 95.6%, in the cracks identification of the rock sample with various angles. The recognition accuracy, the precision, and the recall rate of the U-net model have increased by 0.5%, 2.3%, and 4.3% respectively compared with the Deeplabv3 model. The research results provide new ideas for the intelligent detection of cracks in the rock mass, which offer high-confidence data support for engineering decisions in complex geological environments.