IIUM Repository

Prediction of rockburst intensity grade in deep underground excavation using adaptive boosting classifier

Ahmad, Mahmood and Katman, HerdaYati and Al-Mansob, Ramez and Ahmad, Feezan and Safdar, Muhammad and Alguno, Arnold C. (2022) Prediction of rockburst intensity grade in deep underground excavation using adaptive boosting classifier. Complexity, 2022. pp. 1-10. ISSN 1076-2787 E-ISSN 1099-0526

[img] PDF - Published Version
Restricted to Repository staff only

Download (684kB) | Request a copy
PDF (SCOPUS) - Supplemental Material
Download (300kB) | Preview


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.(e 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. (e output of the AdaBoost model is evaluated using statistical parameters including accuracy and Cohen’s kappa index. (e 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.

Item Type: Article (Journal)
Subjects: T Technology > TA Engineering (General). Civil engineering (General)
T Technology > TA Engineering (General). Civil engineering (General) > TA401 Materials of engineering and construction
Kulliyyahs/Centres/Divisions/Institutes (Can select more than one option. Press CONTROL button): Kulliyyah of Engineering
Depositing User: Dr. Ramez Al-Mansob
Date Deposited: 10 May 2022 15:14
Last Modified: 16 Jun 2022 12:11
URI: http://irep.iium.edu.my/id/eprint/97808

Actions (login required)

View Item View Item


Downloads per month over past year