Publication:
Prediction of Rockburst Intensity Grade in Deep Underground Excavation Using Adaptive Boosting Classifier

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dc.citedby6
dc.contributor.authorAhmad M.en_US
dc.contributor.authorKatman H.Y.en_US
dc.contributor.authorAl-Mansob R.A.en_US
dc.contributor.authorAhmad F.en_US
dc.contributor.authorSafdar M.en_US
dc.contributor.authorAlguno A.C.en_US
dc.contributor.authorid56531227300en_US
dc.contributor.authorid55812804800en_US
dc.contributor.authorid55566434500en_US
dc.contributor.authorid57204650618en_US
dc.contributor.authorid57221085412en_US
dc.contributor.authorid7801308675en_US
dc.date.accessioned2023-05-29T09:40:45Z
dc.date.available2023-05-29T09:40:45Z
dc.date.issued2022
dc.descriptionCompressive strength; Excavation; Forecasting; Underground structures; Boosting classifiers; Case history; Developed model; Early design stages; Key influencing factors; Maximum tangential stress; Project construction; Rockburst intensity; Underground excavation; Uniaxial compressive strength; Adaptive boostingen_US
dc.description.abstractRockburst phenomenon is the primary cause of many fatalities and accidents during deep underground projects constructions. As a result, its prediction at the early design stages plays a significant role in improving safety. The article describes a newly developed model to predict rockburst intensity grade using Adaptive Boosting (AdaBoost) classifier. A database including 165 rockburst case histories was collected from across the world to achieve a comprehensive representation, in which four key influencing factors such as maximum tangential stress of the excavation boundary, uniaxial compressive strength of rock, tensile rock strength, and elastic energy index were selected as the input variables, and the rockburst intensity grade was selected as the output. The output of the AdaBoost model is evaluated using statistical parameters including accuracy and Cohen's kappa index. The applications for the aforementioned approach for predicting the rockburst intensity grade are compared and discussed. Finally, two real-world applications are used to verify the proposed AdaBoost model. It is found that the prediction results are consistent with the actual conditions of the subsequent construction. � 2022 Mahmood Ahmad et al.en_US
dc.description.natureFinalen_US
dc.identifier.ArtNo6156210
dc.identifier.doi10.1155/2022/6156210
dc.identifier.scopus2-s2.0-85130610620
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85130610620&doi=10.1155%2f2022%2f6156210&partnerID=40&md5=aca48c950c1c0ae550d986161aa26674
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/27192
dc.identifier.volume2022
dc.publisherHindawi Limiteden_US
dc.relation.ispartofAll Open Access, Gold, Green
dc.sourceScopus
dc.sourcetitleComplexity
dc.titlePrediction of Rockburst Intensity Grade in Deep Underground Excavation Using Adaptive Boosting Classifieren_US
dc.typeArticleen_US
dspace.entity.typePublication
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