Automatic identification and characteristics analysis of crack tips in rocks with prefabricated defects based on deep learning methods
Mingtao Gao, Minhui Li, Lu Chen, Zihao Guo, Chengyang Guo, Liping Li, Changsen Bu
TL;DR
This paper presents a deep learning approach to automatically detect and analyze crack tips in rocks with prefabricated cracks, improving accuracy and efficiency over traditional methods.
Contribution
The study introduces a novel deep learning-based method for high-precision crack tip identification in rocks with multi-angle prefabricated cracks.
Findings
The CLAHE method outperforms HE and AHE in preprocessing crack images for accurate identification.
The U-Net model achieves 99.4% recognition accuracy, 97.3% precision, and 95.6% recall in crack tip detection.
U-Net outperforms Deeplabv3 by 0.5% in accuracy, 2.3% in precision, and 4.3% in recall for crack identification.
Abstract
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°,…
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Taxonomy
TopicsRock Mechanics and Modeling · Infrastructure Maintenance and Monitoring · Tunneling and Rock Mechanics