Predicting rock mass rating ahead of the tunnel face with Bayesian estimation
The rock mass rating (RMR) system plays a crucial role in geomechanics assessments for tunnel projects. However, conventional methods combining empirical and geostatistical approaches often yield inaccuracies, particularly in areas with weak strata such as faults and karst caves. To address these un...
| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2024-02-01
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| Series: | Frontiers in Earth Science |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/articles/10.3389/feart.2024.1333117/full |
| _version_ | 1797323586001698816 |
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| author | Xiaojun Li Ziyang Chen Li Tang Chao Chen Tao Li Jiaxin Ling Yanyun Lu Yi Rui |
| author_facet | Xiaojun Li Ziyang Chen Li Tang Chao Chen Tao Li Jiaxin Ling Yanyun Lu Yi Rui |
| author_sort | Xiaojun Li |
| collection | DOAJ |
| description | The rock mass rating (RMR) system plays a crucial role in geomechanics assessments for tunnel projects. However, conventional methods combining empirical and geostatistical approaches often yield inaccuracies, particularly in areas with weak strata such as faults and karst caves. To address these uncertainties and errors inherent in empirical techniques, we propose a progressive RMR prediction strategy based on the Bayesian framework. This strategy incorporates three key components: 1) Variogram modeling: utilizing observational data from the excavation face, we construct and update a variogram model to capture the spatial variability of RMR. 2) TSP-RMR statistic model: we integrate a TSP-RMR statistical model into the Bayesian sequential update process. 3) Bayesian maximum entropy (BME) integration: the BME method combines geological information obtained from tunnel surface excavation with tunnel seismic prediction (TSP) data, ultimately enhancing the RMR prediction accuracy. Our methodology is applied to the Laoying rock tunneling project in Yunnan Province, China. Our findings demonstrate that the fusion of soft data and geological interpretation significantly improves the accuracy of RMR predictions. At selected prediction points, the relative error of our method is less than 15% when compared to the traditional Kriging method. This approach holds substantial potential for advancing RMR estimation ahead of tunnel excavation, particularly when advanced geological forecast data are available. |
| first_indexed | 2024-03-08T05:30:44Z |
| format | Article |
| id | doaj.art-3c879ffccea44c84abc9d681866af3d1 |
| institution | Directory Open Access Journal |
| issn | 2296-6463 |
| language | English |
| last_indexed | 2024-03-08T05:30:44Z |
| publishDate | 2024-02-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Earth Science |
| spelling | doaj.art-3c879ffccea44c84abc9d681866af3d12024-02-06T04:59:23ZengFrontiers Media S.A.Frontiers in Earth Science2296-64632024-02-011210.3389/feart.2024.13331171333117Predicting rock mass rating ahead of the tunnel face with Bayesian estimationXiaojun LiZiyang ChenLi TangChao ChenTao LiJiaxin LingYanyun LuYi RuiThe rock mass rating (RMR) system plays a crucial role in geomechanics assessments for tunnel projects. However, conventional methods combining empirical and geostatistical approaches often yield inaccuracies, particularly in areas with weak strata such as faults and karst caves. To address these uncertainties and errors inherent in empirical techniques, we propose a progressive RMR prediction strategy based on the Bayesian framework. This strategy incorporates three key components: 1) Variogram modeling: utilizing observational data from the excavation face, we construct and update a variogram model to capture the spatial variability of RMR. 2) TSP-RMR statistic model: we integrate a TSP-RMR statistical model into the Bayesian sequential update process. 3) Bayesian maximum entropy (BME) integration: the BME method combines geological information obtained from tunnel surface excavation with tunnel seismic prediction (TSP) data, ultimately enhancing the RMR prediction accuracy. Our methodology is applied to the Laoying rock tunneling project in Yunnan Province, China. Our findings demonstrate that the fusion of soft data and geological interpretation significantly improves the accuracy of RMR predictions. At selected prediction points, the relative error of our method is less than 15% when compared to the traditional Kriging method. This approach holds substantial potential for advancing RMR estimation ahead of tunnel excavation, particularly when advanced geological forecast data are available.https://www.frontiersin.org/articles/10.3389/feart.2024.1333117/fullrock mass rate predictiontunnel seismic predictiondynamic Bayesian frameworkmultisource data fusiongeostatistical method |
| spellingShingle | Xiaojun Li Ziyang Chen Li Tang Chao Chen Tao Li Jiaxin Ling Yanyun Lu Yi Rui Predicting rock mass rating ahead of the tunnel face with Bayesian estimation Frontiers in Earth Science rock mass rate prediction tunnel seismic prediction dynamic Bayesian framework multisource data fusion geostatistical method |
| title | Predicting rock mass rating ahead of the tunnel face with Bayesian estimation |
| title_full | Predicting rock mass rating ahead of the tunnel face with Bayesian estimation |
| title_fullStr | Predicting rock mass rating ahead of the tunnel face with Bayesian estimation |
| title_full_unstemmed | Predicting rock mass rating ahead of the tunnel face with Bayesian estimation |
| title_short | Predicting rock mass rating ahead of the tunnel face with Bayesian estimation |
| title_sort | predicting rock mass rating ahead of the tunnel face with bayesian estimation |
| topic | rock mass rate prediction tunnel seismic prediction dynamic Bayesian framework multisource data fusion geostatistical method |
| url | https://www.frontiersin.org/articles/10.3389/feart.2024.1333117/full |
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