|
| Research on Optimization of RMR Classification Method Based on Nth-Order Polynomial Regression Equation |
|
Received:November 25, 2022
Revised:December 23, 2022
Accepted:December 24, 2022
Published Online:August 22, 2023
|
| View Full Text View/Add Comment Download reader |
| DOI： |
| KeyWord:Rock;Mass Quality;Evaluation RMR;Method Nth-Order;Polynomial Regression |
| Author | Institution |
| Lv Guanying |
Changsha Institute of Mining Research Co,Ltd,Changsha |
| Liu Chang |
National Key Laboratory of Metal Mine Safety Technology,Changsha;Changsha Institute of Mining Research Co,Ltd,Changsha |
| He Huansha |
National Key Laboratory of Metal Mine Safety Technology,Changsha;Changsha Institute of Mining Research Co,Ltd,Changsha |
| GuoZeyang |
National Key Laboratory of Metal Mine Safety Technology,Changsha;Changsha Institute of Mining Research Co,Ltd,Changsha |
|
| Hits: 1335 |
| Download times: 741 |
| Abstract: |
| In order to solve the problem that a single evaluation factor of RMR rock mass quality classification method will present a ladder mutation near a specific strength value or integrity index.The relationships among uniaxial compressive strength, point load strength, drilling RQD index and joint spacing in the RMR rock mass quality classification system were nonlinear and continuous fitted by using N-order polynomial regression equation. On this basis, the original evaluation system was optimized.The results show that the optimized RMR rock mass quality classification method eliminates the local mutation in the original evaluation system, and makes the evaluation results have continuity, objectivity and field applicability. This method has provided more scientific and accurate technical support and basis for the evaluation of rock mass quality in many mines. |
| Close |
|
|
|