The DOI matches its Crossref record; 23 of the 23 references it cites were found in a registry or an index.
Evaluation method of rockburst: State-of-the-art literature review
Abstract
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.
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Citations
Cites
Practical estimates of rock mass strength
Hoek, E., & Brown, E. T. (1997) · International Journal of Rock Mechanics and Mining Sciences · doi:10.1016/s1365-1609(97)80069-x · Cited by 3,388
Engineering classification of rock masses for the design of tunnel support
Barton, N., Lien, R., & Lunde, J. (1974) · Rock Mechanics Felsmechanik M�canique des Roches · doi:10.1007/bf01239496 · Cited by 2,979
Jing, L. (2003) · International Journal of Rock Mechanics and Mining Sciences · doi:10.1016/s1365-1609(03)00013-3 · Cited by 1,196
Brittleness determination of rocks by different methods
Hucka, V. J., & Das, B. (1974) · International Journal of Rock Mechanics and Mining Sciences & Geomechanics Abstracts · doi:10.1016/0148-9062(74)91109-7 · Cited by 667
Diederichs, M. S. (2007) · Canadian Geotechnical Journal · doi:10.1139/t07-033 · Cited by 582
Classification of Rockburst in Underground Projects: Comparison of Ten Supervised Learning Methods
Zhou, J., Li, X., & Mitri, H. S. (2016) · Journal of Computing in Civil Engineering · doi:10.1061/(asce)cp.1943-5487.0000553 · Cited by 443
Self-citationZhou, J., Li, X., & Shi, X. (2012) · Safety Science · doi:10.1016/j.ssci.2011.08.065 · Cited by 411
Self-citationKnowledge-based and data-driven fuzzy modeling for rockburst prediction
Adoko, A. C., Gokceoglu, C., Wu, L., & Zuo, Q. J. (2013) · International Journal of Rock Mechanics and Mining Sciences · doi:10.1016/j.ijrmms.2013.02.010 · Cited by 238
Rockburst laboratory tests database — Application of data mining techniques
He, M., Sousa, L. R. E., Miranda, T. F. D. S., & Zhu, G. (2014) · Engineering Geology · doi:10.1016/j.enggeo.2014.12.008 · Cited by 167
Singh, S. P. (1988) · Rock Mechanics and Rock Engineering · doi:10.1007/bf01043119 · Cited by 164
Cited by
Development of Virtual Reality Serious Game for Underground Rock-Related Hazards Safety Training
Liang, Z., Zhou, K., & Gao, K. (2019) · IEEE Access · doi:10.1109/access.2019.2934990 · Cited by 106
Metadata
- Authors
- Zhou, J., Li, X., & Mitri, H. S.
- Venue
- Tunnelling and Underground Space Technology
- Publisher
- Elsevier BV
- Published
- 2018-11
- Volume
- 81
- Pages
- 632-659
- ISSN
- 0886-7798 (ISSN-L)
- OpenAlex
- W2890756131
- Cited by
- 470
- References
- 23
- FWCI
- 24.38
- Type
- journal article
- Language
- en
- Open access
- closed
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- Zhou, J., Li, X., & Mitri, H. S. (2018). Evaluation method of rockburst: State-of-the-art literature review. Tunnelling and Underground Space Technology. https://doi.org/10.1016/j.tust.2018.08.029
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