›› 2016, Vol. 37 ›› Issue (S2): 273-278.doi: 10.16285/j.rsm.2016.S2.033

• Fundamental Theroy and Experimental Research • Previous Articles     Next Articles

Prediction model for extent of excavation damaged zone around roadway based on dimensional analysis

ZHAO Guo-yan, LIANG Wei-zhang, WANG Shao-feng, HONG Chang-shou   

  1. School of Resources and Safety Engineering, Central South University, Changsha, Hunan 410083, China
  • Received:2016-01-15 Online:2016-11-11 Published:2018-06-09
  • Supported by:
    This work was supported by the National Natural Science Foundation of China (51374244).

Abstract: In order to determine the extent of excavation damaged zone around roadway more reasonably, a new prediction method is proposed based on the dimensional analysis theory. Some physical quantities such as rock uniaxial compressive strength Rc, joint coefficient of rock F, unit weight of rock ?, depth of roadway H, maximum horizontal principal stress ?Hmax and span of roadway B are selected; the dimensionless relations among the thickness of excavation damaged zone L and above mentioned physical quantities are established by means of dimensional analysis. Among them, L/H represented that the thickness of excavation damaged zone is proportional to depth of roadway; F represented structural characteristic of surrounding rock; both ?H/Rc and ?Hmax/Rc represented mechanical characteristics of surrounding rock; B/H represented geometric characteristic of roadway. The quantitative function formula among these dimensionless relations is derived by combining with some field test data, the residual sum of square is 0.003 1, and the correlation coefficient is 0.937 9. Finally, the prediction model of the excavation damaged zone is validated with some testing examples from 3 mines. The results show that the calculation results satisfyingly agree with the measured data at the average relative error of 7.38%, which can be of interest from a practical viewpoint.

Key words: excavation damaged zone, dimensional analysis, roadway, prediction model

CLC Number: 

  • TD 353
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