Predicting rockbursts in deep tunnels based on ejection velocity and kinetic energy measurements using advanced machine learning-Reference-Cited by-同舟云学术

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Predicting rockbursts in deep tunnels based on ejection velocity and kinetic energy measurements using advanced machine learning

Published:2024-10 Issue: Volume:166 Page:105671
ISSN:0926-5805
Container-title:Automation in Construction
language:en
Short-container-title:Automation in Construction

Author:

Mahmoodzadeh Arsalan,Ghazouani Nejib,Mohammed Adil Hussein,Ibrahim Hawkar Hashim,Alghamdi Abdulaziz,Albaijan Ibrahim,El Ouni Mohamed Hechmi

Funder

Deanship of Academic Research, University of Jordan

Northern Border University

King Khalid University Deanship of Scientific Research

Publisher

Elsevier BV

Reference47 articles.

1. On continuous health monitoring of bridges under serious environmental variability by an innovative multi-task unsupervised learning method;Entezami;Struct. Infrastruct. Eng.,2023

2. An improvement on feature extraction via time series modeling for structural health monitoring based on unsupervised learning methods;Entezami;Sci. Iran.,2018

3. Probabilistic data self-clustering based on semi-parametric extreme value theory for structural health monitoring;Sarmadi;Mech. Syst. Signal Process.,2023

4. Machine learning algorithms for damage detection under operational and environmental variability;Figueiredo;Struct. Health Monit.,2011

5. Numerical modeling of rockburst near fault zones in deep tunnels;Manouchehrian;Tunn. Undergr. Space Technol.,2018

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