Prediction of Rockburst Intensity Grade in Deep Underground Excavation Using Adaptive Boosting Classifier. (5th May 2022)

• Record Type: Journal Article
• Authors: Ahmad, Mahmood; Katman, Herda Yati; Al-Mansob, Ramez A.; Ahmad, Feezan; Safdar, Muhammad; Alguno, Arnold C.
• Other Names: Craciunescu Teddy Academic Editor.
• Is Part Of: Complexity. Volume 2022(2022)
• Publication Date: 2022-05-05
• Subjects: Chaotic behavior in systems -- Periodicals; Complexity (Philosophy) -- Periodicals; 003
• DOI: 10.1155/2022/6156210
• Languages: English
• ISSNs: 1076-2787

Abstract

Abstract : Rockburst 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.