A review on rockburst prediction and prevention to shape an ontology-based framework for better decision-making for underground excavations

Abstract

With underground engineering projects becoming deeper and more complex, the associated safety problems, especially rockburst, have increasingly increased. Despite decades of research, effective management of rockburst continues to be a formidable challenge in underground excavations. This study presents a scientometric visualization analysis of 2449 papers and conducts a comprehensive review of 336 key studies to explore the state-of-the-art developments in rockburst research. With a primary focus on the prediction and prevention of rockburst, this review identifies existing research gaps and proposes a novel framework aimed at addressing these challenges in underground excavations. The results underscore a critical disconnect between advanced prediction methods and engineering practices, which limits the ability of engineers to carry out reliable assessments of rockburst potential. This disconnection prevents the prompt development of targeted prevention strategies, further aggravated by inadequate data sharing across large-scale projects. The review also describes the limitations of relying solely on data-driven methodologies to address the complex challenges in the lifecycle management of underground excavations. To overcome these challenges, this study proposes an innovative framework based on an ontological knowledge base. This framework is designed to integrate multisource data and diverse analysis techniques, exploring the means toward better decision-making in future digital underground projects.

Keywords

decision support system / ontology / rockburst / scientometric analysis / underground engineering

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