Random Forest-based Stability Analysis Model of Geotechnical Tunnel Enclosure
- DOI
- 10.2991/978-94-6463-793-9_86How to use a DOI?
- Keywords
- geotechnical engineering; perimeter rock stability; random forests
- Abstract
In this study, a geotechnical tunnel surrounding rock stability analysis model is established based on the random forest algorithm in order to improve the accuracy and reliability of surrounding rock stability prediction in tunnel engineering. Through testing and data collection of geotechnical parameters, the surrounding rock stability is classified and analyzed using the random forest model. The experimental results show that the Random Forest is superior to the traditional RMR method and Q system in terms of overall prediction accuracy, feature adaptability and the ability to handle abnormal samples, especially in the classification of V-class surrounding rock, which shows significant advantages. The research results provide a more scientific decision-making basis for the assessment of surrounding rock stability in tunnel engineering, and have strong practical application value.
- Copyright
- © 2025 The Author(s)
- Open Access
- Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.
Cite this article
TY - CONF AU - Yang Yu AU - Qingxin Pang PY - 2025 DA - 2025/07/28 TI - Random Forest-based Stability Analysis Model of Geotechnical Tunnel Enclosure BT - Proceedings of the 2025 8th International Conference on Traffic Transportation and Civil Architecture (ICTTCA 2025) PB - Atlantis Press SP - 1004 EP - 1014 SN - 2589-4943 UR - https://doi.org/10.2991/978-94-6463-793-9_86 DO - 10.2991/978-94-6463-793-9_86 ID - Yu2025 ER -