Content area
This study presents an intelligent identification method for shale crack networks based on transfer learning. Focuses on investigating the main physical parameters of shale similar materials based on the similarity theory and the physical parameters of shale. Fracture network images obtained from shale-like materials. The fracture network images are then preprocessed using image processing technology to generate a high-quality shale image dataset. A deep learning transfer recognition model, based on ResNet-50, is constructed to detect model performance using these shale fracture network images. Experimental results and reliability analyses demonstrate that the ResNet-50 based deep learning transfer model achieves an accuracy of 87% for shale images, indicating high recognition accuracy and fast model convergence. The proposed intelligent shale crack identification method exhibits robustness and generalization ability, making it suitable for efficient fracture identification in geological, road, and bridge deck projects.
Details
Model accuracy;
Shales;
Similarity theory;
Mathematical models;
Physical properties;
Bridge decks;
Image quality;
Deep learning;
Identification methods;
Image processing;
Parameters;
Mechanical properties;
Principles;
Accuracy;
Datasets;
Vision systems;
Identification;
Concrete;
Civil engineering;
Asphalt pavements;
Measurement techniques;
Cracks;
Machine learning;
Oil shale;
Oil reserves;
Artificial intelligence;
Neural networks;
Classification;
Earth science;
Algorithms;
Informatics;
Natural gas reserves
1 Zhongyuan University of Technology, School of Civil Engineering and Architecture, ZhengzhouHenan, China (GRID:grid.449903.3) (ISNI:0000 0004 1758 9878)