Novel multifractal-based classification model for the quality grades of surrounding rock within tunnels

Abstract

Understanding the variation patterns of tunnel boring machine (TBM) operational parameters is crucial for assessing engineering geological conditions and quality grades of surrounding rock within tunnels. Studying the multifractal characteristics of the TBM operational parameters can help identify the patterns, but the relevant research has not yet been explored. This paper proposed a novel classification model for quality grades of surrounding rock in TBM tunnels based on multifractal analysis theory. Initially, the statistical characteristics of eight TBM cycle data with different grades of surrounding rock were explored. Subsequently, the method of calculating and analyzing the multifractal characteristic parameters of the TBM operational data was deduced and summarized. The research results showed that the TBM operational parameters of cutterhead torque, total thrust, advance rate, and cutterhead rotation speed have significant multifractal characteristics. Its multifractal dimension, midpoint slope of the generalized fractal spectrum, and singularity strength range can be used to evaluate the surrounding rock grades of the tunnel. Finally, a novel classification model for the tunnel surrounding rocks based on the multifractal characteristic parameters was proposed using the multiple linear regression method, and the model was verified through four TBM cycle data containing different surrounding rock grades. The results showed that the proposed multifractal-based classification model for tunnel surrounding rocks has high accuracy and applicability. This study not only achieves multifractal feature representation and surrounding rock classification for TBM operational parameters but also holds the potential for adaptive adjustment of TBM operational parameters and automated tunneling applications.

Keywords

Surrounding rock classification / Tunnel boring machine / Operational parameter / Multifractal characteristics / Multiple linear regression

Data availability

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

CRediT authorship contribution statement

Junjie Ma: Writing - review & editing, Writing - original draft, Methodology, Conceptualization, Formal analysis. Tianbin Li: Writing - review & editing, Supervision, Project administration, Methodology, Funding acquisition, Data curation. Zhen Zhang: Writing - review & editing, Methodology, Formal analysis. Roohollah Shirani Faradonbeh: Writing - review & editing, Supervision, Formal analysis. Mostafa Sharifzadeh: Writing - review & editing. Chunchi Ma: Writing - review & editing, Data curation.

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

This study is supported by the National Natural Science Foundation of China (Grant Nos. 42130719 and U19A20111).

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