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...
- Autores:
-
Zhou, Yu
Wang, Tingling
- Tipo de recurso:
- Article of journal
- Fecha de publicación:
- 2017
- Institución:
- Universidad Nacional de Colombia
- Repositorio:
- Universidad Nacional de Colombia
- Idioma:
- spa
- OAI Identifier:
- oai:repositorio.unal.edu.co:unal/63577
- Acceso en línea:
- https://repositorio.unal.edu.co/handle/unal/63577
http://bdigital.unal.edu.co/64023/
- Palabra clave:
- 55 Ciencias de la tierra / Earth sciences and geology
Probabilistic neural network (PNN)
Rockburst
Prediction
Red Neuronal Probabilística
fracturamiento de rocas
predicción
- Rights
- openAccess
- License
- Atribución-NoComercial 4.0 Internacional
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PNN-based Rockburst Prediction Model and Its Applications |
| title |
PNN-based Rockburst Prediction Model and Its Applications |
| spellingShingle |
PNN-based Rockburst Prediction Model and Its Applications 55 Ciencias de la tierra / Earth sciences and geology Probabilistic neural network (PNN) Rockburst Prediction Red Neuronal Probabilística fracturamiento de rocas predicción |
| title_short |
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_sort |
PNN-based Rockburst Prediction Model and Its Applications |
| dc.creator.fl_str_mv |
Zhou, Yu Wang, Tingling |
| dc.contributor.author.spa.fl_str_mv |
Zhou, Yu Wang, Tingling |
| dc.subject.ddc.spa.fl_str_mv |
55 Ciencias de la tierra / Earth sciences and geology |
| topic |
55 Ciencias de la tierra / Earth sciences and geology Probabilistic neural network (PNN) Rockburst Prediction Red Neuronal Probabilística fracturamiento de rocas predicción |
| dc.subject.proposal.spa.fl_str_mv |
Probabilistic neural network (PNN) Rockburst Prediction Red Neuronal Probabilística fracturamiento de rocas predicción |
| 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. |
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2017 |
| dc.date.issued.spa.fl_str_mv |
2017-07-01 |
| dc.date.accessioned.spa.fl_str_mv |
2019-07-02T21:55:04Z |
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2019-07-02T21:55:04Z |
| dc.type.spa.fl_str_mv |
Artículo de revista |
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http://purl.org/coar/resource_type/c_2df8fbb1 |
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info:eu-repo/semantics/article |
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info:eu-repo/semantics/publishedVersion |
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http://purl.org/coar/resource_type/c_6501 |
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http://purl.org/coar/version/c_970fb48d4fbd8a85 |
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Text |
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http://purl.org/redcol/resource_type/ART |
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http://purl.org/coar/resource_type/c_6501 |
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publishedVersion |
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ISSN: 2339-3459 |
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https://repositorio.unal.edu.co/handle/unal/63577 |
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http://bdigital.unal.edu.co/64023/ |
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ISSN: 2339-3459 |
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https://repositorio.unal.edu.co/handle/unal/63577 http://bdigital.unal.edu.co/64023/ |
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spa |
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spa |
| dc.relation.spa.fl_str_mv |
https://revistas.unal.edu.co/index.php/esrj/article/view/65216 |
| dc.relation.ispartof.spa.fl_str_mv |
Universidad Nacional de Colombia Revistas electrónicas UN Earth Sciences Research Journal Earth Sciences Research Journal |
| dc.relation.references.spa.fl_str_mv |
Zhou, Yu and Wang, Tingling (2017) PNN-based Rockburst Prediction Model and Its Applications. Earth Sciences Research Journal, 21 (3). pp. 141-146. ISSN 2339-3459 |
| dc.rights.spa.fl_str_mv |
Derechos reservados - Universidad Nacional de Colombia |
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http://purl.org/coar/access_right/c_abf2 |
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Atribución-NoComercial 4.0 Internacional |
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http://creativecommons.org/licenses/by-nc/4.0/ |
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info:eu-repo/semantics/openAccess |
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Atribución-NoComercial 4.0 Internacional Derechos reservados - Universidad Nacional de Colombia http://creativecommons.org/licenses/by-nc/4.0/ http://purl.org/coar/access_right/c_abf2 |
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application/pdf |
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Universidad Nacional de Colombia - Sede Bogotá - Facultad de Ciencias - Departamento de Geociencia |
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Atribución-NoComercial 4.0 InternacionalDerechos reservados - Universidad Nacional de Colombiahttp://creativecommons.org/licenses/by-nc/4.0/info:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2Zhou, Yu31a5f365-f011-44f5-b096-0ae5906683f4300Wang, Tingling52f69429-6707-40b3-a890-75a1c337e93c3002019-07-02T21:55:04Z2019-07-02T21:55:04Z2017-07-01ISSN: 2339-3459https://repositorio.unal.edu.co/handle/unal/63577http://bdigital.unal.edu.co/64023/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.El fracturamiento o explosión de rocas es uno de los principales problemas en ingeniería geológica que amenaza significativamente la seguridad de una construcción. La predicción del fracturamiento de rocas es importante para la seguridad de los trabajadores y el equipamiento en túneles. En este artículo se propone un nuevo modelo de predicción de fracturamiento de rocas basado en una red neuronal probabilística (PNN por sus siglas en inglés) para determinar la posible ocurrencia e intensidad de uno de estos eventos en proyectos subterráneos. La PNN se desarrolló con base en un criterio Bayesiano para la clasificación multivariada de patrones. Debido a que la PNN tiene las ventajas de una menor complejidad de adiestramiento, estabilidad, rápida convergencia y simplicidad en su construcción, se puede adecuar en la predicción del fracturamiento de rocas. Algunos factores principales de control, como la fuerza máxima tangencial de rocas, la resistencia de compresión uniaxial, la fuerza de tensión uniaxial, y el índice de energía elástica de las rocas fueron escogidos como los vectores característicos de la PNN. El modelo se obtuvo a través del adiestramiento de datos sobre fracturamiento de rocas en proyectos subterráneos en diferentes localidades. Otras datos también se analizaron con el modelo. Los resultados de la evaluación se ajustan a los registros observados. Simultáneamente, se utilizaron dos aplicaciones prácticas para verificar el método propuesto. Los resultados de la predicción son similares a los de métodos existentes, un factor que además se presentó en las pruebas de campo, lo que demuestra la efectividad y la aplicabilidad de la metodología propuesta.application/pdfspaUniversidad Nacional de Colombia - Sede Bogotá - Facultad de Ciencias - Departamento de Geocienciahttps://revistas.unal.edu.co/index.php/esrj/article/view/65216Universidad Nacional de Colombia Revistas electrónicas UN Earth Sciences Research JournalEarth Sciences Research JournalZhou, Yu and Wang, Tingling (2017) PNN-based Rockburst Prediction Model and Its Applications. Earth Sciences Research Journal, 21 (3). pp. 141-146. ISSN 2339-345955 Ciencias de la tierra / Earth sciences and geologyProbabilistic neural network (PNN)RockburstPredictionRed Neuronal Probabilísticafracturamiento de rocaspredicciónPNN-based Rockburst Prediction Model and Its ApplicationsArtículo de revistainfo:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501http://purl.org/coar/resource_type/c_2df8fbb1http://purl.org/coar/version/c_970fb48d4fbd8a85Texthttp://purl.org/redcol/resource_type/ARTORIGINAL65216-352569-2-PB.pdfapplication/pdf1066626https://repositorio.unal.edu.co/bitstream/unal/63577/1/65216-352569-2-PB.pdf46d1c1aeb6f403e78b9cc22f86e564bdMD51THUMBNAIL65216-352569-2-PB.pdf.jpg65216-352569-2-PB.pdf.jpgGenerated Thumbnailimage/jpeg7418https://repositorio.unal.edu.co/bitstream/unal/63577/2/65216-352569-2-PB.pdf.jpg62172f49d1304ddf588c5c17fa287994MD52unal/63577oai:repositorio.unal.edu.co:unal/635772024-04-29 23:11:16.413Repositorio Institucional Universidad Nacional de Colombiarepositorio_nal@unal.edu.co |