Intelligent hybrid model to classify failure modes of overstressed rock masses in deep engineering

Abstract

Squeezing, slabbing, and strainburst are typical failure modes of overstressed rock masses in deep rock excavation engineering. This study considered intact rock properties to evaluate squeezing, slabbing, and strainburst, owing to the effectiveness and availability of these parameters. Hybrid models combining the Jaya algorithm and support vector machine (JA-SVM) were proposed to predict the failure modes of overstressed rock masses based on the collected database. JA-SVM model achieved a training accuracy of 0.970 and a testing accuracy of 0.875. Ranking system and Taylor diagrams showed that the developed hybrid model was superior to other machine learning (ML) models, including SVM, artificial neural network, etc. Receiver operator characteristic curves suggested that JA-SVM had a more powerful ability to predict strainburst and slabbing compared to other widely applied ML techniques. Performed sensitive analysis revealed that the brittleness index and elastic modulus were vital factors in estimating failure modes. The developed model can be applied to identify failure modes of overstressed rock masses in the initial phases of a deep underground project, and appropriate support measures can be prepared beforehand based on estimation results.

Keywords

squeezing / slabbing / strainburst / support vector machine / Jaya algorithm

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