Publication: Prediction of Rockburst Intensity Grade in Deep Underground Excavation Using Adaptive Boosting Classifier
Date
2022
Authors
Ahmad M.
Katman H.Y.
Al-Mansob R.A.
Ahmad F.
Safdar M.
Alguno A.C.
Journal Title
Journal ISSN
Volume Title
Publisher
Hindawi Limited
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. � 2022 Mahmood Ahmad et al.
Description
Compressive 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 boosting