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

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...

Full description

Bibliographic Details
Main Authors: Behshad Jodeiri Shokri, Ali Mirzaghorbanali, Kevin McDougall, Warna Karunasena, Hadi Nourizadeh, Shima Entezam, Shahab Hosseini, Naj Aziz
Format: Article
Language:English
Published: MDPI AG 2024-10-01
Series:Applied Sciences
Subjects:
fully grouted rock bolt
XGBoost
axial load bearing capacity
machine learning
prediction
Online Access:https://www.mdpi.com/2076-3417/14/21/9925

Internet

https://www.mdpi.com/2076-3417/14/21/9925

Similar Items