A Semi-Supervised Clustering Algorithm for Underground Disaster Monitoring and Early Warning

Due to complex geological conditions and external environmental factors, the structural safety of tunnels faces many challenges. In order to achieve real-time monitoring and early warning for tunnel safety, this paper proposes a semi-supervised clustering algorithm, named SSCME. First, in the traditional concept drift detection stage, this algorithm improves traditional methods by utilizing data distribution to calculate the concept deviation and accurately identify four different types of concept drift. Second, in the incremental update stage, the EM algorithm is further optimized to remove the outlier data used for incrementally updating the classifier, thus resolving the sensitivity issue of DBSCAN in parameter selection. Finally, a large number of sensors are installed in multiple tunnels to collect data and construct datasets. The experimental results on multiple datasets demonstrate that, compared with existing baseline methods, this algorithm has higher effectiveness and reliability.