Probabilistic classification prediction of rockburst intensity in a deep buried high geo-stress rock tunnel during engineering investigation
Rockburst is a dynamic process of a sudden and rapid release of elastic strain energy stored in hard rock mass during underground excavation. The occurrence of rockburst disaster during tunnel construction will cause serious consequences such as casualties, equipment damage and construction delay. W...
| Main Authors: | , , , , , |
|---|---|
| Format: | Article |
| Language: | zho |
| Published: |
Editorial Office of Hydrogeology & Engineering Geology
2022-11-01
|
| Series: | Shuiwen dizhi gongcheng dizhi |
| Subjects: | |
| Online Access: | https://www.swdzgcdz.com/en/article/doi/10.16030/j.cnki.issn.1000-3665.202111027 |
| _version_ | 1811170286116012032 |
|---|---|
| author | Weijun LIU Junqi FAN Tianbin LI Peng GUO Peng ZENG Guanghong JU |
| author_facet | Weijun LIU Junqi FAN Tianbin LI Peng GUO Peng ZENG Guanghong JU |
| author_sort | Weijun LIU |
| collection | DOAJ |
| description | Rockburst is a dynamic process of a sudden and rapid release of elastic strain energy stored in hard rock mass during underground excavation. The occurrence of rockburst disaster during tunnel construction will cause serious consequences such as casualties, equipment damage and construction delay. With a large number of deep-buried long tunnels to be constructed in southwestern mountainous areas of China, the prediction of rockburst disaster is of great importance. In this paper, to fulfil the requirement of tunnel alignment and design during engineering investigation stage, on the premise of the availability of rockburst prediction indexes in this stage, the Bayesian network is used to reflect the relationship between rockburst intensity and various influencing factors. Based on 473 groups of rockburst cases, the naive Bayesian probability classification model is constructed to predict the rockburst intensity by using four prediction indexes—geo-stress, geological structure, surrounding rock grade and surrounding rock strength. The prediction accuracy of the model is found to be 84.47% using the 10-fold cross validation method. At the same time, this model is applied to the rockburst section of Paomashan No. 1 Tunnel of Ya’an—Yecheng Expressway. The results show that the prediction accuracy is 85.71% in the 28 tunnel section applications, and the established Bayesian network model has a good prediction performance. The proposed method can provide a good support to the rockburst prediction during the investigation of deep-buried long tunnels located in Southwest China. |
| first_indexed | 2024-04-10T16:55:31Z |
| format | Article |
| id | doaj.art-aa5481717b8a4b638907dba46cbee9d0 |
| institution | Directory Open Access Journal |
| issn | 1000-3665 |
| language | zho |
| last_indexed | 2024-04-10T16:55:31Z |
| publishDate | 2022-11-01 |
| publisher | Editorial Office of Hydrogeology & Engineering Geology |
| record_format | Article |
| series | Shuiwen dizhi gongcheng dizhi |
| spelling | doaj.art-aa5481717b8a4b638907dba46cbee9d02023-02-07T08:15:05ZzhoEditorial Office of Hydrogeology & Engineering GeologyShuiwen dizhi gongcheng dizhi1000-36652022-11-0149611412310.16030/j.cnki.issn.1000-3665.202111027202111027Probabilistic classification prediction of rockburst intensity in a deep buried high geo-stress rock tunnel during engineering investigationWeijun LIU0Junqi FAN1Tianbin LI2Peng GUO3Peng ZENG4Guanghong JU5College of Environment and Civil Engineering, Chengdu University of Technology, Chengdu, Sichuan 610059, ChinaInstitute of Defense Engineering, Academy of Military Sciences, Luoyang, Henan 471023, ChinaCollege of Environment and Civil Engineering, Chengdu University of Technology, Chengdu, Sichuan 610059, ChinaInstitute of Defense Engineering, Academy of Military Sciences, Luoyang, Henan 471023, ChinaCollege of Environment and Civil Engineering, Chengdu University of Technology, Chengdu, Sichuan 610059, ChinaNorthwest Engineering Corporation Limited, Xi’an, Shaanxi 710000, ChinaRockburst is a dynamic process of a sudden and rapid release of elastic strain energy stored in hard rock mass during underground excavation. The occurrence of rockburst disaster during tunnel construction will cause serious consequences such as casualties, equipment damage and construction delay. With a large number of deep-buried long tunnels to be constructed in southwestern mountainous areas of China, the prediction of rockburst disaster is of great importance. In this paper, to fulfil the requirement of tunnel alignment and design during engineering investigation stage, on the premise of the availability of rockburst prediction indexes in this stage, the Bayesian network is used to reflect the relationship between rockburst intensity and various influencing factors. Based on 473 groups of rockburst cases, the naive Bayesian probability classification model is constructed to predict the rockburst intensity by using four prediction indexes—geo-stress, geological structure, surrounding rock grade and surrounding rock strength. The prediction accuracy of the model is found to be 84.47% using the 10-fold cross validation method. At the same time, this model is applied to the rockburst section of Paomashan No. 1 Tunnel of Ya’an—Yecheng Expressway. The results show that the prediction accuracy is 85.71% in the 28 tunnel section applications, and the established Bayesian network model has a good prediction performance. The proposed method can provide a good support to the rockburst prediction during the investigation of deep-buried long tunnels located in Southwest China.https://www.swdzgcdz.com/en/article/doi/10.16030/j.cnki.issn.1000-3665.202111027deep-buried hard rock tunnelinvestigation stagerockburst disasterprobabilistic classification predictionbayesian network |
| spellingShingle | Weijun LIU Junqi FAN Tianbin LI Peng GUO Peng ZENG Guanghong JU Probabilistic classification prediction of rockburst intensity in a deep buried high geo-stress rock tunnel during engineering investigation Shuiwen dizhi gongcheng dizhi deep-buried hard rock tunnel investigation stage rockburst disaster probabilistic classification prediction bayesian network |
| title | Probabilistic classification prediction of rockburst intensity in a deep buried high geo-stress rock tunnel during engineering investigation |
| title_full | Probabilistic classification prediction of rockburst intensity in a deep buried high geo-stress rock tunnel during engineering investigation |
| title_fullStr | Probabilistic classification prediction of rockburst intensity in a deep buried high geo-stress rock tunnel during engineering investigation |
| title_full_unstemmed | Probabilistic classification prediction of rockburst intensity in a deep buried high geo-stress rock tunnel during engineering investigation |
| title_short | Probabilistic classification prediction of rockburst intensity in a deep buried high geo-stress rock tunnel during engineering investigation |
| title_sort | probabilistic classification prediction of rockburst intensity in a deep buried high geo stress rock tunnel during engineering investigation |
| topic | deep-buried hard rock tunnel investigation stage rockburst disaster probabilistic classification prediction bayesian network |
| url | https://www.swdzgcdz.com/en/article/doi/10.16030/j.cnki.issn.1000-3665.202111027 |
| work_keys_str_mv | AT weijunliu probabilisticclassificationpredictionofrockburstintensityinadeepburiedhighgeostressrocktunnelduringengineeringinvestigation AT junqifan probabilisticclassificationpredictionofrockburstintensityinadeepburiedhighgeostressrocktunnelduringengineeringinvestigation AT tianbinli probabilisticclassificationpredictionofrockburstintensityinadeepburiedhighgeostressrocktunnelduringengineeringinvestigation AT pengguo probabilisticclassificationpredictionofrockburstintensityinadeepburiedhighgeostressrocktunnelduringengineeringinvestigation AT pengzeng probabilisticclassificationpredictionofrockburstintensityinadeepburiedhighgeostressrocktunnelduringengineeringinvestigation AT guanghongju probabilisticclassificationpredictionofrockburstintensityinadeepburiedhighgeostressrocktunnelduringengineeringinvestigation |