Title: Big data automatic analysis system and its applications in rockburst experiment

Authors: Yu Zhang; Yanping Bai; Manchao He; Zhaoyong Lv; Yongzhen Li

Addresses: School of Electrical and Information Engineering, Beijing University of Civil Engineering and Architecture, 100044, Beijing, China; State Key Laboratory for GeoMechanics and Deep Underground Engineering, China University of Mining and Technology, 100083, Beijing, China ' College of Management, Capital Normal University, 100048, Beijing, China ' State Key Laboratory for GeoMechanics and Deep Underground Engineering, China University of Mining and Technology, 100083, Beijing, China ' Department of Computer Teaching and Network Information, Beijing University of Civil Engineering and Architecture, 100044, Beijing, China ' Department of Computer Teaching and Network Information, Beijing University of Civil Engineering and Architecture, 100044, Beijing, China

Abstract: In 2006, State Key Laboratory for GeoMechanics and Deep Underground Engineering, GDLab for short, successfully reproduced the rockburst procedure indoors. Since then, a series of valuable research results has been gained in the area of rockburst mechanism. At the same time, there are some dilemmas, such as data storage dilemma, data analysis dilemma and prediction accuracy dilemma. GDLab has accumulated more than 500 TB data of rockburst experiment. But so far, the amount of analysed data is less than 5%. The primary cause of these dilemmas is the large amount of experimental data in the procedure of study of rockburst. In this paper, a novel big data automatic analysis system for rockburst experiment is proposed. Various modules and algorithms are designed and realised. Theoretical analysis and experimental research show that big data automatic analysis system for rockburst experiment can improve the existing research mechanism of rockburst. It also can make many impossible things become possible. The work of this paper lays a theoretical foundation for rockburst mechanism research.

Keywords: rockburst; experiment data; big data; automatic analysis.

DOI: 10.1504/IJCSE.2019.099070

International Journal of Computational Science and Engineering, 2019 Vol.18 No.4, pp.321 - 331

Received: 16 Jun 2016
Accepted: 05 Sep 2016
Published online: 15 Apr 2019 *

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