Improved expert system of rockburst intensity level prediction based on machine learning and data-driven: Supported by 1114 rockburst cases in 197 rock underground projects

Abstract

Accurate prediction of rockburst intensity levels is crucial for ensuring the safety of deep hard rock engineering construction. This paper introduced an expert system for rockburst intensity level prediction that employs machine learning algorithms as the basis for its inference rules. The system comprises four modules: a database, a repository, an inference engine, and an interpreter. A database containing 1114 rockburst cases was used to construct 357 datasets that serve as the repository for the expert system. Additionally, 19 types of machine learning algorithms were used to establish 6783 micro-models to construct cognitive rules within the inference engine. By integrating probability theory and marginal analysis, a fuzzy scoring method based on the SoftMax function was developed and applied to the interpreter for rockburst intensity level prediction, effectively restoring the continuity of rockburst characteristics. The research results indicate that ensemble algorithms based on decision trees are more effective in capturing the characteristics of rockburst. Key factors for accurate prediction of rockburst intensity include uniaxial compressive strength, elastic energy index, the maximum principal stress, tangential stress, and their composite indicators. The accuracy of the proposed rockburst intensity level prediction expert system was verified using 20 engineering rockburst cases, with predictions aligning closely with the actual rockburst intensity levels.

Keywords

rock mechanics / rockburst / rockburst intensity level prediction / expert system / machine learning / supervised learning

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