Username   Password       Forgot your password?  Forgot your username? 


Automated Collaborative Analysis System of Rockburst Mechanism based on Big Data

Volume 14, Number 7, July 2018, pp. 1431-1438
DOI: 10.23940/ijpe.18.07.p6.14311438

Yu Zhanga,b, Hongwei Dinga, Yange Wanga, Fuqiang Renb, Yongzhen Lia, and Zhaoyong Lva

aSchool of Electrical and Information Engineering, Beijing University of Civil Engineering and Architecture, Beijing, 100044, China
bState Key Laboratory in China for GeoMechanics and Deep Underground Engineering, China University of Mining & Technology, Beijing, 100083, China

(Submitted on April 12, 2018; Revised on May 29, 2018; Accepted on June 19, 2018)


In recent years, with the increase of the resource exploitation, mining depth is getting deeper and deeper. Meanwhile, the lives of mining workers have been threatened strongly. In order to reduce the probability of rockburst, researchers have carried out in-depth research on rockburst. He Manchao, the academician of the State Key Laboratory for GeoMechanics and Deep Underground Engineering, has initially simulated the occurrence of rockburst in the laboratory as well as studied the mechanism of rockburst. Because the amount of data accumulated in the experiment is as large as 1000T, using these valuable experimental data becomes a difficult problem. Therefore, we have introduced big data technology into the field of rockburst. We have designed and realized the automated collaborative analysis system of the rockburst mechanism based on big data. We have used acoustic emission sensors as data collection methods and selected the multi-task online learning algorithm for data processing and analyzing. We have achieved the selection of the inflection point in the process of force changes using Matlab. In addition, the inflection point to be checked in the system can obtain the corresponding part of analysis diagrams. The theoretical analyses and experimental studies show that the automated collaborative analysis system can have an obvious influence on rockburst data processing, which provides a good foundation for the study of the rockburst mechanism.


References: 15

          1. M. Amin, and M. Cai, “Analysis of Rockburst in Tunnels Subjected to Static and Dynamic Loads,” Journal of Rock Mechanics and Geotechnical Engineering 9.6(2017):1031-1040
          2. Y. T. Chen, J. Qin, J. G. Li et al, “Application of AE Technique in Coal and Gas Outburst Prediction,” MINING SAFETY&ENVIRONMENTAL PROTECTION, 2017,(06):1-5
          3. Y. Fu, H. C. Li, X.P. Wu et al, “Detecting APT Attacks: A Survey from the Perspective of Big Data Analysis,” Journal on Communications, 2015,36(11):1-14.
          4. H. Guo, J. Liu, L. Zhao, “ Big Data Acquisition under Failures in FiWi Enhanced Smart Grid,” IEEE Transactions on Emerging Topics in Computing, 2017, PP(99):1-1.
          5. B. G. He, Zelig. R, Y. H. Hatzor et al, “ Rockburst Generation in Discontinuous Rock Masses,” Rock Mechanics & Rock Engineering, 2016(10):1-22.
          6. W. W. Jiang, Z. H. Su, X. X. Chen et al, “Stability Prediction Model of Rock Mass Based on Microseismic Monitoring System,” MINING R&D ,2016,36(11):41-44.
          7. G. Li, C. J. Zhou, Y. Zhang et al, “Review of Rockburst in Underground Engineering,” Advances in Science and Technology of Water Resources, ,2013,33(03):77-83+94
          8. A. S. Panah, R. V. Schyndel, T. Sellis, “ Towards an Asynchronous Aggregation-capable Watermark for end-to-end Protection of Big Data Streams,” Future Generation Computer Systems, 2017, 72:288-304.
          9. K. Raichura, and N. Padhariya, “ Big Cache: A Cache-based Big Data Management in Mobile Networks,” International Journal of Mobile Communications, 2017, 15(1):49.
          10. K. N. Trubetskoy, et al, “Rockburst Prediction based on Submicron Particle Emission in Rocks under Deformation and Failure,” Gornyi Zhurnal 6(2017):16-20.
          11. “Xi Jinping to the Fourth World Internet Conference Congratulation Letter,” Available at, Last accessed on Dec 3, 2017
          12. H. Yang, M. R. Lyu, I. King, “ Efficient Online Learning for Multitask Feature Selection,” Acm Transactions on Knowledge Discovery from Data, 2013, 7(2):1-27
          13. P. L. Zhang, R. J. Wang, Y. C. Jiang et al, “Acoustic Emission Technology Research of Pipeline Leak Detection,” Journal of Gansu Sciences, 2017,29(03):86-91.
          14. W. H. Zhu, N. Ma, Y. Y. Xia et al, “Model Tests on Rock Burst Using Gas-liquid Composite Loading,” Chinese Journal of Rock Mechanics and Engineering, 2017,36(01):159-166.
          15. X. Zhang, Y. Wang, H. S. Hu et al, “Research and Application of Acoustic Emission Technique in Railway System Inspection,” Journal of Applied Acoustics, 2017,36(03):189-199.


                  Please note : You will need Adobe Acrobat viewer to view the full articles.Get Free Adobe Reader

                  Download this file (06-IJPE-2018-07-06.pdf)IJPE-2018-07-06[Automated Collaborative Analysis System of Rockburst Mechanism based on Big Data]668 Kb
                  This site uses encryption for transmitting your passwords.