Enhanced safety assessment on tunnel excavation via refined rock mass parameter identification

Abstract

This study employs computer vision and deep learning techniques to execute the refined extraction and quantification of rock mass information in tunnel faces. The integration of contact measurement data and surrounding environmental parameters leads to a proposal for rock mass quality prediction, utilizing integrated machine learning techniques. Subsequently, a 3D model is established by incorporating tunnel face features and environmental data. The safety factor of rock mass excavation is calculated through the utilization of the strength reduction method, and the analysis of rock mass stability on the continuous tunnel face is performed, considering factors such as rock stress and joint sliding. The investigation of variation patterns of excavation safety factors, influenced by multiple modelling factors, is conducted through the utilization of a response surface design method in 46 experimental studies. The research reveals the accurate characterization of complex fissure occurrence obtained in the field through a discrete fracture network. Furthermore, a negative correlation between the safety factor of tunnel excavation and the grade of surrounding rock is observed, with an increase in grade resulting in a decrease in the safety factor. The response surface method effectively discloses polynomial correlations between various parameters such as inclination angle, dip direction, spacing, density, number of groups, and the safety factor. This elucidates the impact patterns of these parameters and their coupling states on the safety factor. The study provides significant insights into the intelligent evaluation of safety for continuous tunnel excavation.

Keywords

Rock tunnel / Feature identification / Safety evaluation / Deep learning / Response surface method

Data availability

The data that support the findings of this study are available from the corresponding author upon reasonable request.

CRediT authorship contribution statement

Hongwei Huang: Supervision, Conceptualization, Funding acquisition, Formal analysis. Tongjun Yang: Project administration, Data curation. Jiayao Chen: Visualization, Funding acquisition, Data curation, Writing - review & editing, Software, Investigation, Writing - original draft, Methodology, Formal analysis. Zhongkai Huang: Investigation, Writing - review & editing. Chen Wu: Validation, Methodology, Software. Jianhong Man: Writing - review & editing, Investigation, Validation.

Declaration of competing interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Acknowledgement

The research presented in this paper is supported by the Fundamental Research Funds for the Central Universities (Grant No. 2025JBMC041), Natural Science Foundation Committee Program of China (Grant Nos. 52308388 and 51778474), and Open Fund for Key Laboratories of the Ministry of Education (No. KLE-TJGE-B2305).

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