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Development of a Semi-quantitative Framework to Assess Rockburst Risk Using Risk Matrix and Logistic Model Tree

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Abstract

Rockburst is a common dynamic phenomenon due to high stress in deep underground spaces, which has many destructive effects based on the intensity of occurrence. Therefore, by predicting the risk of this phenomenon and using the necessary measures, the destructive effects of this phenomenon can be reduced. In this paper, a semi-quantitative risk matrix model has been developed for assessing rockburst risk. For quantitatively determining the probability and consequence of the rockburst, as two main components of the risk matrix, two models were developed using the logistic model tree (LMT) method. These models were developed based on a database containing the most effective parameters on the occurrence of rockburst. After developing the models, their accuracy was evaluated by performance criteria including accuracy (AC), specificity (SP), sensitivity (SE), Matthew’s correlation coefficient (MCC), and receiver operating characteristic curve. The results showed that the two developed models have high performances (for the probability model, the values of AC, SE, SP, and MCC are 0.96, 0.79, 0.99, and 0.85, respectively, and for the consequence model the results are 0.93, 0.82, 0.95, and 0.81, respectively) and also comparing two developed models with previous models revealed that both models have acceptable accuracy. Finally, based on the developed models, a semi-quantitative framework was provided to evaluate the risk of rockburst using risk matrix. This framework can be applied to reduce the casualties resulting from rockburst in underground spaces during design and construction phases.

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  1. Department of Mining Engineering, Isfahan University of Technology, Isfahan, 8415683111, Iran

    Mohammad Hossein Kadkhodaei & Ebrahim Ghasemi

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  1. Mohammad Hossein Kadkhodaei
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Kadkhodaei, M.H., Ghasemi, E. Development of a Semi-quantitative Framework to Assess Rockburst Risk Using Risk Matrix and Logistic Model Tree. Geotech Geol Eng 40, 3669–3685 (2022). https://doi.org/10.1007/s10706-022-02122-9

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