Research on Risk Identification and Early Warning of Bolt Failure in Coal Mine Roadway

To overcome challenges in spatial positioning and early warning of failure risks in coal mine roadway support bolts, and to prevent mine pressure accidents in surrounding rock while ensuring the safety and stability of mining faces, FBG (Fiber Bragg Grating) sensing technology was utilized to monitor the axial force of on-site support bolts. Using statistical algorithms, this study identified risks and conducted early warning analyses for support bolt failures, providing a theoretical basis for improving roadway support systems and predicting surrounding rock disasters. A dataset of bolt axial forces was gathered. The Gaussian process regression algorithm was utilized to identify and predict failure risks in support bolts, demonstrating its feasibility for coal mine roadway support system failure detection. A spatial 3D regression algorithm was used to analyze on-site monitoring data of roadway support bolts. The axial force was monitored along the entire length, embedded depth, and height of the roadway bolts. The axial force of bolts near the fully mechanized mining face showed significant fluctuations, with pronounced stress variations in the surrounding rock of locally arranged bolt sections. When bolts are detected in an abnormal safety state, appropriate support measures must be taken.