Data-Driven Optimised XGBoost for Predicting the Performance of Axial Load Bearing Capacity of Fully Cementitious Grouted Rock Bolting Systems

This article investigates the application of eXtreme gradient boosting (XGBoost) and hybrid metaheuristics optimisation techniques to predict the axial load bearing capacity of fully grouted rock bolting systems. For this purpose, a comprehensive dataset of 72 pull-out tests was built, considering v...

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Bibliographic Details

Published in:	Applied sciences Vol. 14; no. 21; p. 9925
Main Authors:	Jodeiri Shokri, Behshad, Mirzaghorbanali, Ali, McDougall, Kevin, Karunasena, Warna, Nourizadeh, Hadi, Entezam, Shima, Hosseini, Shahab, Aziz, Naj
Format:	Journal Article
Language:	English
Published:	Basel MDPI AG 01-11-2024
Subjects:	Accuracy Algorithms Artificial intelligence axial load bearing capacity fully grouted rock bolt Load machine learning Mathematical optimization Mechanical properties Mining engineering Neural networks Performance evaluation prediction XGBoost Australia
Online Access:	Get full text
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