A knowledge-data dually driven paradigm for accurate identification of key blocks in complex rock slopes

Accurate identification and effective support of key blocks are crucial for ensuring the stability and safety of rock slopes. The number of structural planes and rock blocks were reduced in previous studies. This impairs the ability to characterize complex rock slopes accurately and inhibits the identification of key blocks. In this paper, a knowledge-data dually driven paradigm for accurate identification of key blocks in complex rock slopes is proposed. Our basic idea is to integrate key block theory into data-driven models based on finely characterizing structural features to identify key blocks in complex rock slopes accurately. The proposed novel paradigm consists of (1) representing rock slopes as graph-structured data based on complex systems theory, (2) identifying key nodes in the graph-structured data using graph deep learning, and (3) mapping the key nodes of graph-structured data to corresponding key blocks in the rock slope. Verification experiments and real-case applications are conducted by the proposed method. The verification results demonstrate excellent model performance, strong generalization capability, and effective classification results. Moreover, the real case application is conducted on the northern slope of the Yanqianshan Iron Mine. The results show that the proposed method can accurately identify key blocks in complex rock slopes, which can provide a decision-making basis and rational recommendations for effective support and instability prevention of rock slopes, thereby ensuring the stability of rock engineering and the safety of life and property.