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Geostatistical method for inferring RMR ahead of tunnel face excavation using dynamically exposed geological information
AbstractRock Mass Rating (RMR) is a rock mass classification system that is often used to select appropriate excavation methods and rock support systems in tunnel projects. This paper presents a geostatistical method for inferring RMR values quantitatively ahead of excavation of the tunnel face. The study makes full use of geological information exposed on excavated tunnel faces to capture the spatial correlation of rock mass quality and later predicts the RMR value using the kriging method. Predictions are constantly updated during the tunnel construction process. The advantages of the proposed method are as follows: (1) The RMR prediction uncertainty is quantified by accounting for spatial variability and model uncertainty; therefore, the resulting prediction can consider the geological conditions of the worst scenario; (2) The spatial variability of the geological condition is represented as a variogram model that is updated by observation data on the new excavated faces, and as the tunnel advances, the RMR prediction accuracy improves; and (3) The periodicity of geological conditions can be considered in RMR prediction. The proposed method is applied to a rock tunneling project, the Mingtang tunnel in Anhui province, China. The method achieves approximately 80% prediction accuracy; therefore, it has high potential as a tool for predicting RMR information ahead of tunnel face excavation.
HighlightsA geostatistical method for inferring RMR values ahead of the tunnel face using geological information is presented.The spatial variability of geological condition is updated by observation data on the new excavated faces.The RMR prediction uncertainty is quantified by accounting for spatial variability and model uncertainty.The prediction of RMR values allows for periodicity of geological conditions.
Geostatistical method for inferring RMR ahead of tunnel face excavation using dynamically exposed geological information
AbstractRock Mass Rating (RMR) is a rock mass classification system that is often used to select appropriate excavation methods and rock support systems in tunnel projects. This paper presents a geostatistical method for inferring RMR values quantitatively ahead of excavation of the tunnel face. The study makes full use of geological information exposed on excavated tunnel faces to capture the spatial correlation of rock mass quality and later predicts the RMR value using the kriging method. Predictions are constantly updated during the tunnel construction process. The advantages of the proposed method are as follows: (1) The RMR prediction uncertainty is quantified by accounting for spatial variability and model uncertainty; therefore, the resulting prediction can consider the geological conditions of the worst scenario; (2) The spatial variability of the geological condition is represented as a variogram model that is updated by observation data on the new excavated faces, and as the tunnel advances, the RMR prediction accuracy improves; and (3) The periodicity of geological conditions can be considered in RMR prediction. The proposed method is applied to a rock tunneling project, the Mingtang tunnel in Anhui province, China. The method achieves approximately 80% prediction accuracy; therefore, it has high potential as a tool for predicting RMR information ahead of tunnel face excavation.
HighlightsA geostatistical method for inferring RMR values ahead of the tunnel face using geological information is presented.The spatial variability of geological condition is updated by observation data on the new excavated faces.The RMR prediction uncertainty is quantified by accounting for spatial variability and model uncertainty.The prediction of RMR values allows for periodicity of geological conditions.
Geostatistical method for inferring RMR ahead of tunnel face excavation using dynamically exposed geological information
Chen, Jianqin (Autor:in) / Li, Xiaojun (Autor:in) / Zhu, Hehua (Autor:in) / Rubin, Yoram (Autor:in)
Engineering Geology ; 228 ; 214-223
01.08.2017
10 pages
Aufsatz (Zeitschrift)
Elektronische Ressource
Englisch
| British Library Online Contents | 2017
| British Library Online Contents | 2003