Evaluation method of rockburst: State-of-the-art literature review
By Jian Zhou, Xibing Li, H. Mitri. Tunnelling and Underground Space Technology, 2018
436 citations recorded by the source corpus.
Abstract The evaluation of rockburst is becoming increasingly important as mining activities reach greater depths below the ground surface. In the literature, rockburst assessment has been tackled by many researchers with various methods. However, there has not been a study that examines and compares different rockburst assessment methods. In this paper, rockburst classification and its varying definitions are briefly summarized. A comprehensive review of the research efforts since 1965 then follows. This includes empirical, numerical, statistical and intelligent classification methods. Of particular significance is that in all the above-mentioned techniques, the review highlights the source of data, timeline of study and the comparative performance of various techniques in terms of their prediction accuracy wherever available. The review also lists current achievements, limitations and some promising directions for future research.
Subfields: Management, Monitoring, Policy and Law, Mechanics of Materials, Ocean Engineering
View Paper: https://www.semanticscholar.org/paper/bfb8d5678d56958e5afbdee9da316f6d17ca5b83
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In Management, Monitoring, Policy and Law
In Mechanics of Materials
In Ocean Engineering
Citations in the corpus
- Examining Hybrid and Single SVM Models with Different Kernels to Predict Rock Brittleness (2020, background): “In recent years, many researchers have applied soft computing (SC), artificial intelligence (AI), and machine learning (ML) techniques to solve science and engineering problems [8, 26– 75 ].”
- State-of-the-art review of soft computing applications in underground excavations (2020, background): “Assessment of rock-burst and the risk prediction has been investigated by Su et al. (2010), and Zhou et al. (2012, 2016, 2018).”