Analysis of key geological structures and rockburst prediction method
Abstract Rockburst disasters generally occur within specific geological structures. Consequently, predicting and evaluating the potential rockbursts necessitates more focuses on the “geological carrier”. Considering the geological structure effects of rockbursts, a novel prediction method based on t...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-05-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-025-99744-9 |
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| _version_ | 1849312044652691456 |
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| author | Chunchi Ma Yang Yuan Xiang Ji Feng Peng Ziquan Chen Hang Zhang |
| author_facet | Chunchi Ma Yang Yuan Xiang Ji Feng Peng Ziquan Chen Hang Zhang |
| author_sort | Chunchi Ma |
| collection | DOAJ |
| description | Abstract Rockburst disasters generally occur within specific geological structures. Consequently, predicting and evaluating the potential rockbursts necessitates more focuses on the “geological carrier”. Considering the geological structure effects of rockbursts, a novel prediction method based on the key geological structures was proposed by combining numerical simulation with neural network. This study systematically investigated typical geological structures and geomechanical modes of rockbursts. An evaluation index for the relative energy release effect of rockbursts was established through the numerical simulation, which can be adopted to analyze the sensitivity of the key structural parameters. Subsequently, a surrogate model was developed using GA-BP neural network combined with Latin hypercube sampling to accurately represent the relationship between the key structural parameters and rockburst effects. Using the rockburst intensity classification scheme and the Monte Carlo method, the samples within the dangerous range of rockbursts were identified. By analyzing the rockburst sample points of various grades, the confidence intervals for the key structural parameters were interpreted, with the prediction method developed for identifying the key geological structures. This innovative method facilitated the rapid "dictionary-style" prediction of rockbursts in underground engineering. It comprehensively considered the impacts of geological structures on rockbursts and could offer a new pathway for precise rockburst disaster prediction. |
| format | Article |
| id | doaj-art-cf3d8df0952743f4bacd36ba0e779ad2 |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-cf3d8df0952743f4bacd36ba0e779ad22025-08-20T03:53:12ZengNature PortfolioScientific Reports2045-23222025-05-0115112410.1038/s41598-025-99744-9Analysis of key geological structures and rockburst prediction methodChunchi Ma0Yang Yuan1Xiang Ji2Feng Peng3Ziquan Chen4Hang Zhang5State Key Laboratory of Geohazard Prevention and Geoenvironment Protection, Chengdu University of TechnologyState Key Laboratory of Geohazard Prevention and Geoenvironment Protection, Chengdu University of TechnologyCGN LuFeng Nuclear Power Co., LtdState Key Laboratory of Geohazard Prevention and Geoenvironment Protection, Chengdu University of TechnologyKey Laboratory of Transportation Tunnel Engineering, Ministry of Education, Southwest Jiaotong UniversitySichuan Development Emerging Industrial Park Investment Construction Management Co., LtdAbstract Rockburst disasters generally occur within specific geological structures. Consequently, predicting and evaluating the potential rockbursts necessitates more focuses on the “geological carrier”. Considering the geological structure effects of rockbursts, a novel prediction method based on the key geological structures was proposed by combining numerical simulation with neural network. This study systematically investigated typical geological structures and geomechanical modes of rockbursts. An evaluation index for the relative energy release effect of rockbursts was established through the numerical simulation, which can be adopted to analyze the sensitivity of the key structural parameters. Subsequently, a surrogate model was developed using GA-BP neural network combined with Latin hypercube sampling to accurately represent the relationship between the key structural parameters and rockburst effects. Using the rockburst intensity classification scheme and the Monte Carlo method, the samples within the dangerous range of rockbursts were identified. By analyzing the rockburst sample points of various grades, the confidence intervals for the key structural parameters were interpreted, with the prediction method developed for identifying the key geological structures. This innovative method facilitated the rapid "dictionary-style" prediction of rockbursts in underground engineering. It comprehensively considered the impacts of geological structures on rockbursts and could offer a new pathway for precise rockburst disaster prediction.https://doi.org/10.1038/s41598-025-99744-9Rockburst predictionGeological structure effectRelative energy release effectNumerical simulationConfidence interval |
| spellingShingle | Chunchi Ma Yang Yuan Xiang Ji Feng Peng Ziquan Chen Hang Zhang Analysis of key geological structures and rockburst prediction method Scientific Reports Rockburst prediction Geological structure effect Relative energy release effect Numerical simulation Confidence interval |
| title | Analysis of key geological structures and rockburst prediction method |
| title_full | Analysis of key geological structures and rockburst prediction method |
| title_fullStr | Analysis of key geological structures and rockburst prediction method |
| title_full_unstemmed | Analysis of key geological structures and rockburst prediction method |
| title_short | Analysis of key geological structures and rockburst prediction method |
| title_sort | analysis of key geological structures and rockburst prediction method |
| topic | Rockburst prediction Geological structure effect Relative energy release effect Numerical simulation Confidence interval |
| url | https://doi.org/10.1038/s41598-025-99744-9 |
| work_keys_str_mv | AT chunchima analysisofkeygeologicalstructuresandrockburstpredictionmethod AT yangyuan analysisofkeygeologicalstructuresandrockburstpredictionmethod AT xiangji analysisofkeygeologicalstructuresandrockburstpredictionmethod AT fengpeng analysisofkeygeologicalstructuresandrockburstpredictionmethod AT ziquanchen analysisofkeygeologicalstructuresandrockburstpredictionmethod AT hangzhang analysisofkeygeologicalstructuresandrockburstpredictionmethod |