Surrounding Rock Squeezing Classification in Underground Engineering Using a Hybrid Paradigm of Generative Artificial Intelligence and Deep Ensemble Learning

Shouye Cheng, Xin Yin (), Feng Gao and Yucong Pan
Additional contact information
Shouye Cheng: Research Institute of Mine Construction, Tiandi Science and Technology Company Limited, Beijing 100013, China
Xin Yin: School of Civil Engineering, Wuhan University, Wuhan 430072, China
Feng Gao: Research Institute of Mine Construction, Tiandi Science and Technology Company Limited, Beijing 100013, China
Yucong Pan: School of Civil Engineering, Wuhan University, Wuhan 430072, China

Mathematics, 2024, vol. 12, issue 23, 1-18

Abstract: Surrounding rock squeezing is a common geological disaster in underground excavation projects (e.g., TBM tunneling and deep mining), which has adverse effects on construction safety, schedule, and property. To predict the squeezing of the surrounding rock accurately and quickly, this study proposes a hybrid machine learning paradigm that integrates generative artificial intelligence and deep ensemble learning. Specifically, conditional tabular generative adversarial network is devised to solve the problems of data shortage and class imbalance for data augmentation at the data level, and the deep random forest is built based on the augmented data for subsequent squeezing classification. A total of 139 historical squeezing cases are collected worldwide to validate the efficacy of the proposed modeling paradigm. The results reveal that this paradigm achieves a prediction accuracy of 92.86% and a macro F 1 -score of 0.9292. In particular, the individual F 1 -scores on strong squeezing and extremely strong squeezing are more than 0.9, with excellent prediction reliability for high-intensity squeezing. Finally, a comparative analysis with traditional machine learning techniques is conducted and the superiority of this paradigm is further verified. This study provides a valuable reference for surrounding rock squeezing classification under a limited data environment.

Keywords: underground engineering; squeezing classification; limited data quality; generative artificial intelligence; deep ensemble learning (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/2227-7390/12/23/3832/pdf (application/pdf)
https://www.mdpi.com/2227-7390/12/23/3832/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jmathe:v:12:y:2024:i:23:p:3832-:d:1536493

Access Statistics for this article

Mathematics is currently edited by Ms. Emma He

More articles in Mathematics from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().