A platform for research: civil engineering, architecture and urbanism
A platform for research: civil engineering, architecture and urbanism
The Comprehensive Prediction Model of Rockburst Tendency in Tunnel Based on Optimized Unascertained Measure Theory
Abstract Rockburst is an extremely complicated dynamic instability phenomenon. It is one of the common rock mechanics problems in high geostress areas. The accuracy of the rockburst tendency prediction is generally restricted by the evaluation factor or method. In this study, the set pair analysis theory is used to optimize the identification criteria of attribute in unascertained measure theory. Combined with the information entropy theory, a comprehensive multi-factor prediction model was established to predict the rockburst intensity of evaluation objects and forecast the development trend of rockburst from dynamic prediction. The turchaninov criterion (σθ + σL)/Rc, the russenes method σθ/Rc, the strength brittleness coefficient B, the elastic strain energy Wet and the rock quality designation RQD were presented to constitute comprehensive evaluation multi-factor. The proposed model was used in the Dawoshan Tunnel of Jinwen Railway which was divided into six sections with different lithology areas. The evaluation factors require to collect geostress parameters of the tunnel at different buried depths and the mechanical parameters of rock mass. All parameters can be obtained by carry out the field geostress test and laboratory testing. By doing so, the affiliated grade of rockburst intensity can be identified. The results of presented prediction model showed the high accuracy and practicality. It is a meaningful discovery for researching rockburst tendency prediction.
The Comprehensive Prediction Model of Rockburst Tendency in Tunnel Based on Optimized Unascertained Measure Theory
Abstract Rockburst is an extremely complicated dynamic instability phenomenon. It is one of the common rock mechanics problems in high geostress areas. The accuracy of the rockburst tendency prediction is generally restricted by the evaluation factor or method. In this study, the set pair analysis theory is used to optimize the identification criteria of attribute in unascertained measure theory. Combined with the information entropy theory, a comprehensive multi-factor prediction model was established to predict the rockburst intensity of evaluation objects and forecast the development trend of rockburst from dynamic prediction. The turchaninov criterion (σθ + σL)/Rc, the russenes method σθ/Rc, the strength brittleness coefficient B, the elastic strain energy Wet and the rock quality designation RQD were presented to constitute comprehensive evaluation multi-factor. The proposed model was used in the Dawoshan Tunnel of Jinwen Railway which was divided into six sections with different lithology areas. The evaluation factors require to collect geostress parameters of the tunnel at different buried depths and the mechanical parameters of rock mass. All parameters can be obtained by carry out the field geostress test and laboratory testing. By doing so, the affiliated grade of rockburst intensity can be identified. The results of presented prediction model showed the high accuracy and practicality. It is a meaningful discovery for researching rockburst tendency prediction.
The Comprehensive Prediction Model of Rockburst Tendency in Tunnel Based on Optimized Unascertained Measure Theory
Jia, Qinji (author) / Wu, Li (author) / Li, Bo (author) / Chen, Chunhui (author) / Peng, Yaxiong (author)
2019
Article (Journal)
English
Comprehensive Prediction of Rockburst in High-Speed Railway Tunnel
| Trans Tech Publications | 2013
Comprehensive Prediction of Rockburst in High-Speed Railway Tunnel
| Tema Archive | 2013
Geological Structural Surface Evaluation Model Based on Unascertained Measure
| DOAJ | 2019
A Novel Rockburst Tendency Index Based on LURR
| Springer Verlag | 2024