In underground mining, especially in entry-type excavations, the instability of surrounding rock structures can lead to incalculable losses. As a crucial tool for stability analysis in entry-type excavations, the critical span graph must be updated to meet more stringent engineering requirements. Given this, this study introduces the support vector machine (SVM), along with multiple ensemble (bagging, adaptive boosting, and stacking) and optimization (Harris hawks optimization (HHO), cuckoo search (CS)) techniques, to overcome the limitations of the traditional methods. The analysis indicates that the hybrid model combining SVM, bagging, and CS strategies has a good prediction performance, and its test accuracy reaches 0.86. Furthermore, the partition scheme of the critical span graph is adjusted based on the CS-BSVM model and 399 cases. Compared with previous empirical or semi-empirical methods, the new model overcomes the interference of subjective factors and possesses higher interpretability. Since relying solely on one technology cannot ensure prediction credibility, this study further introduces genetic programming (GP) and kriging interpolation techniques. The explicit expressions derived through GP can offer the stability probability value, and the kriging technique can provide interpolated definitions for two new subclasses. Finally, a prediction platform is developed based on the above three approaches, which can rapidly provide engineering feedback.

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Keywords: Entry-type excavations, Critical span graph, Stability evaluation, Machine learning, Support vector machine