PNN-based Rockburst Prediction Model and Its Applications
Rock burst is one of main engineering geological problems significantly threatening the safety of construction. Prediction of rock burst is always an important issue concerning the safety of workers and equipment in tunnels. In this paper, a novel PNN-based rock burst prediction model is proposed to...
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| Language: | English |
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Universidad Nacional de Colombia
2017-07-01
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| Series: | Earth Sciences Research Journal |
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| Online Access: | https://revistas.unal.edu.co/index.php/esrj/article/view/65216 |
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| author | Yu Zhou Tingling Wang |
| author_facet | Yu Zhou Tingling Wang |
| author_sort | Yu Zhou |
| collection | DOAJ |
| description | Rock burst is one of main engineering geological problems significantly threatening the safety of construction. Prediction of rock burst is always an important issue concerning the safety of workers and equipment in tunnels. In this paper, a novel PNN-based rock burst prediction model is proposed to determine whether rock burst will happen in the underground rock projects and how much the intensity of rock burst is. The probabilistic neural network (PNN) is developed based on Bayesian criteria of multivariate pattern classification. Because PNN has the advantages of low training complexity, high stability, quick convergence, and simple construction, it can be well applied in the prediction of rock burst. Some main control factors, such as rocks’ maximum tangential stress, rocks’ uniaxial compressive strength, rocks’ uniaxial tensile strength, and elastic energy index of rock are chosen as the characteristic vector of PNN. PNN model is obtained through training data sets of rock burst samples which come from underground rock project in domestic and abroad. Other samples are tested with the model. The testing results agree with the practical records. At the same time, two real-world applications are used to verify the proposed method. The results of prediction are same as the results of existing methods, just same as what happened in the scene, which verifies the effectiveness and applicability of our proposed work. |
| format | Article |
| id | doaj-art-1be15afe6fc14d2ca98e338f8b278e02 |
| institution | OA Journals |
| issn | 1794-6190 2339-3459 |
| language | English |
| publishDate | 2017-07-01 |
| publisher | Universidad Nacional de Colombia |
| record_format | Article |
| series | Earth Sciences Research Journal |
| spelling | doaj-art-1be15afe6fc14d2ca98e338f8b278e022025-08-20T02:19:15ZengUniversidad Nacional de ColombiaEarth Sciences Research Journal1794-61902339-34592017-07-0121314114610.15446/esrj.v21n3.6521646764PNN-based Rockburst Prediction Model and Its ApplicationsYu Zhou0Tingling Wang1School of Electric Power, North China University of Water Resources and Electric Power, Zhengzhou 450045, ChinaSchool of Electric Power, North China University of Water Resources and Electric Power, Zhengzhou 450045, ChinaRock burst is one of main engineering geological problems significantly threatening the safety of construction. Prediction of rock burst is always an important issue concerning the safety of workers and equipment in tunnels. In this paper, a novel PNN-based rock burst prediction model is proposed to determine whether rock burst will happen in the underground rock projects and how much the intensity of rock burst is. The probabilistic neural network (PNN) is developed based on Bayesian criteria of multivariate pattern classification. Because PNN has the advantages of low training complexity, high stability, quick convergence, and simple construction, it can be well applied in the prediction of rock burst. Some main control factors, such as rocks’ maximum tangential stress, rocks’ uniaxial compressive strength, rocks’ uniaxial tensile strength, and elastic energy index of rock are chosen as the characteristic vector of PNN. PNN model is obtained through training data sets of rock burst samples which come from underground rock project in domestic and abroad. Other samples are tested with the model. The testing results agree with the practical records. At the same time, two real-world applications are used to verify the proposed method. The results of prediction are same as the results of existing methods, just same as what happened in the scene, which verifies the effectiveness and applicability of our proposed work.https://revistas.unal.edu.co/index.php/esrj/article/view/65216Probabilistic neural network (PNN)RockburstPrediction |
| spellingShingle | Yu Zhou Tingling Wang PNN-based Rockburst Prediction Model and Its Applications Earth Sciences Research Journal Probabilistic neural network (PNN) Rockburst Prediction |
| title | PNN-based Rockburst Prediction Model and Its Applications |
| title_full | PNN-based Rockburst Prediction Model and Its Applications |
| title_fullStr | PNN-based Rockburst Prediction Model and Its Applications |
| title_full_unstemmed | PNN-based Rockburst Prediction Model and Its Applications |
| title_short | PNN-based Rockburst Prediction Model and Its Applications |
| title_sort | pnn based rockburst prediction model and its applications |
| topic | Probabilistic neural network (PNN) Rockburst Prediction |
| url | https://revistas.unal.edu.co/index.php/esrj/article/view/65216 |
| work_keys_str_mv | AT yuzhou pnnbasedrockburstpredictionmodelanditsapplications AT tinglingwang pnnbasedrockburstpredictionmodelanditsapplications |