Skip to main content
Log in

Load–unload response ratio characteristics of rock materials and their application in prediction of rockburst proneness

  • Original Paper
  • Published:
Bulletin of Engineering Geology and the Environment Aims and scope Submit manuscript

Abstract

The load–unload response ratio (LURR) theory was introduced to study the rockburst proneness of rock materials, and a rockburst proneness criterion was proposed in this project. First, ten rocks (including three types of granite, three types of sandstone, one kind of limestone, and three types of marble) were chosen on which to carry out the uniaxial step load–unload test, and then a calculation method for LURR at different load–unload points based on a data-fitting algorithm was presented. The LURR-strain curves of the ten rocks were divided into the decrease stage, the steady stage, and the increase stage, and the starting point of the increase stage was defined as the LURR S-R (start rise) point. Subsequently, the LURR difference method was put forward to determine the LURR S-R point scientifically. Finally, the relationship between the lag time ratio (the lag time ratio was defined as the ratio of the time from the LURR S-R point to the peak strength point to the time of the whole loading period) and the degree of rockburst proneness for ten rocks was investigated; the results show that there is an obvious correspondence between the lag time ratio and the degree of rockburst proneness. Then, a rockburst proneness criterion based on the lag time ratio index was proposed. In addition, the real-time pre-warning of rockburst proneness based on the dynamic lag time ratio index was also presented and analysed.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16
Fig. 17
Fig. 18

Similar content being viewed by others

References

  • Bao C, Jiang A, Tang C et al (2011) Study of acoustic emission characteristics of limestone under cycle uniaxial loading–unload perturbation. Chin J Rock Mech Eng s2:3871–3877 (in Chinese)

    Google Scholar 

  • Cai MF (2016) Prediction and prevention of rockburst in metal mines: a case study of Sanshandao gold mine. J Rock Mech Geotech Eng 8(2):204–211

    Article  Google Scholar 

  • Cai M, Kaiser PK, Tasaka Y et al (2004) Generalized crack initiation and crack damage stress thresholds of brittle rock masses near underground excavations. Int J Rock Mech Min Sci 41(5):833–847

    Article  Google Scholar 

  • Durrheim RJ, Haile A, Roberts MKC, Schweizer JK, Spottiswoode SM, Klokow JW (1998) Violent failure a remnant in a deep South African gold mine. Tectonophysics 289(1-3):105–116

    Article  Google Scholar 

  • Gong FQ, Luo Y, Li XB et al (2018a) Experimental simulation investigation on rockburst induced by spalling failure in deep circular tunnels. Tunn Undergr Space Technol 81:413–427

    Article  Google Scholar 

  • Gong FQ, Yan JY, Li XB (2018b) A new criterion of rock burst proneness based on the linear energy storage law and the residual elastic energy index. Chin J Rock Mech Eng 37(9):1993–2014 (in Chinese)

    Google Scholar 

  • He K, Wang S, Du W et al (2008) The dynamic parameter of rainfall: its importance in the prediction of colluvial landslides. Bull Eng Geol Environ 67(3):345–351

    Article  Google Scholar 

  • He K, Zhao M, Zhang Y et al (2017) Unload–load displacement response ratio parameter and its application in prediction of debris landslide induced by rainfall. Environ Earth Sci 76(1):55. https://doi.org/10.1007/s12665-016-6372-0

    Article  Google Scholar 

  • Jiang Q, Feng XT, Xiang TB et al (2010) Rockburst characteristics and numerical simulation based on a new energy index: a case study of a tunnel at 2,500 m depth. Bull Eng Geol Environ 69(3):381–388

    Article  Google Scholar 

  • Kidybiński A (1981) Bursting liability indices of coal. Int J Rock Mech Min 18(4):295–304

    Article  Google Scholar 

  • Kusaka A, Garvey R, Ozbay U (2013) A study of the influence of tunnel shape on rockburst proneness using numerical modeling. Eur J Appl Math 22(4):291–316

    Google Scholar 

  • Li SJ, Feng XT, Li Z et al (2012) In situ, monitoring of rockburst nucleation and evolution in the deeply buried tunnels of Jinping II hydropower station. Eng Geol 137-138(7):85–96

    Article  Google Scholar 

  • Li T, Sun XH, Lv YG et al (2011) Predicting the risk of strong mining-induced seismicity based on the rock load/unload response theory. J Univ Sci Technol b 33(11):1307–1311 (in Chinese)

    Google Scholar 

  • Martin CD, Chandler NA (1994) The progressive fracture of Lac du Bonnet granite. Int J Rock Mech Min Sci Geomech Abstr 31(6):643–659

    Article  Google Scholar 

  • Nicksiar M, Martin CD (2013) Crack initiation stress in low porosity crystalline and sedimentary rocks. Eng Geol 154(2):64–76

    Article  Google Scholar 

  • Ortlepp WD, Stacey TR (1994) Rockburst mechanisms in tunnels and shafts. Tunn Undergr Space Technol 9(1):59–65

    Article  Google Scholar 

  • Panthi KK (2012) Evaluation of rock bursting phenomena in a tunnel in the Himalayas. Bull Eng Geol Environ 71(4):761–769

    Article  Google Scholar 

  • Tang L, Wang W (2002) New rock burst proneness index. Chin J Rock Mech Eng 21(6):874–878 (in Chinese)

    Google Scholar 

  • Wang JA, Park HD (2001) Comprehensive prediction of rockburst based on analysis of strain energy in rocks. Tunn Undergr Space Technol 16(1):49–57

    Article  Google Scholar 

  • Wang YC, Yin C, Peter M, Yin XC, Peng K (2004) Spatial and temporal variation of LURR and its implication for the proneness of earthquake occurrence in Southern California. Pure Appl Geophys 161:2281–2293

    Google Scholar 

  • Xie HP, Ju Y, Li LY (2005) Criteria for strength and structural failure of rocks based on energy dissipation and energy release principles. Chin J Rock Mech Eng 24(17):3003–3010

    Google Scholar 

  • Xue Y, Li Z, Li S et al (2017) Prediction of rock burst in underground caverns based on rough set and extensible comprehensive evaluation. Bull Eng Geol Environ 17:1–13

    Google Scholar 

  • Xu J, Jiang J, Xu N et al (2017) A new energy index for evaluating the tendency of rockburst and its engineering application. Eng Geol 230:46–54

    Article  Google Scholar 

  • Yin XC (1987) A new approach to earthquake prediction. Earthquake Research in China 3:1–7 (in Chinese)

    Google Scholar 

  • Yin XC, Chen XZ, Song ZP (1994) The load/unload response ratio (LURR) theory and its application to earthquake prediction. J Earthq Predict Res 3(3):325–333

    Google Scholar 

  • Yin XC, Chen XZ, Song ZP (1995) A new approach to earthquake prediction-the load/unload response ratio (LURR) theory. Pure Appl Geophys 145(3/4):701–715

    Article  Google Scholar 

  • Yin XC, Wang YC, Peng KY et al (2000) Development of a new approach to earthquake prediction: load/unload response ratio (LURR) theory. Pure Appl Geophys 157(11-12):2365–2383

    Article  Google Scholar 

  • Yin XC, Yin C (1991) The precursor of instability for nonlinear systems and its application to earthquake prediction. Sci China Chem 83(8):55–60

    Google Scholar 

  • Yin XC, Zhang LP, Zhang Y et al (2010) The peak point of LURR and its significance. Concurr Comp Pract E 22(12):1549–1558

    Article  Google Scholar 

  • Yin XC, Liu Y, Mora P et al (2013) New progress in LURR—Integrating with the dimensional method. Pure Appl Geophys 170(1-2):229–236

    Article  Google Scholar 

  • Yin XC (2015) Load/unload response ratio theory and its application. Science Press, Beijing (in Chinese)

    Google Scholar 

  • Zhang J, Peng W, Liu F et al (2016) Monitoring rock failure processes using the Hilbert–Huang transform of acoustic emission signals. Rock Mech Rock Eng 49(2):427–442

    Article  Google Scholar 

  • Zhang WJ, Chen YM, Zhan LT (2006) Loading/unloading response ratio theory applied in predicting deep-seated landslides triggering. Eng Geol 82(4):234–240

    Article  Google Scholar 

  • Zhao F, He MC (2017) Size effects on granite behavior under unloading rockburst test. Bull Eng Geol Environ 76(3):1–15

    Article  Google Scholar 

  • Zhou H, Meng F, Zhang C, Hu D, Yang F, Lu J (2015) Analysis of rockburst mechanisms induced by structural planes in deep tunnels. Bull Eng Geol Environ 74(4):1435–1451

    Article  Google Scholar 

  • Zhu FC, Ai CC, Liu BX, Tian FL (2016) Study on load/unload response ratio of brittle rocks under different stress paths. Metal Mine 45(4):52–57 (in Chinese)

    Google Scholar 

Download references

Acknowledgements

This work was supported by the National Natural Science Foundation of China (Grant No. 41472269 and 41877272).

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Feng-Qiang Gong.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Gong, FQ., Wu, C., Luo, S. et al. Load–unload response ratio characteristics of rock materials and their application in prediction of rockburst proneness. Bull Eng Geol Environ 78, 5445–5466 (2019). https://doi.org/10.1007/s10064-019-01474-6

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10064-019-01474-6

Keywords

Navigation