Predicting triaxial compressive strength of high-temperature treated rock using machine learning techniques

Predicting triaxial compressive strength of high-temperature treated rock using machine learning techniques

The accurate prediction of the strength of rocks after high-temperature treatment is important for the safety maintenance of rock in deep underground engineering. Five machine learning (ML) techniques were adopted in this study, i.e. back propagation neural network (BPNN), AdaBoost-based classificat...

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Bibliographic Details
Main Authors: Xunjian Hu, Junjie Shentu, Ni Xie, Yujie Huang, Gang Lei, Haibo Hu, Panpan Guo, Xiaonan Gong
Format: Article
Language:English
Published: Elsevier 2023-08-01
Series:Journal of Rock Mechanics and Geotechnical Engineering
Subjects:
Machine learning (ML)
Triaxial compressive strength
Temperature
Confining pressure
Crack damage stress
Online Access:http://www.sciencedirect.com/science/article/pii/S167477552200227X

Internet

http://www.sciencedirect.com/science/article/pii/S167477552200227X

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