# Classification of the Surrounding Rock Based on Image Processing Analysis and Transfer Learning

## TL;DR

This study improves the classification of tunnel surrounding rock using image processing and transfer learning, leading to more accurate and standardized results.

## Contribution

A novel classification method for surrounding rock using fractal theory and transfer learning is proposed.

## Key findings

- Fractal theory effectively reveals the complexity of weak layers and joints in surrounding rock.
- Image analysis corrects errors in traditional rebound meter strength values.
- A transfer learning model enables rapid and accurate classification of tunnel surrounding rock.

## Abstract

Currently, standardized classification methods of surrounding rock are relatively insufficient. The classification of surrounding rock mainly relies on the subjective judgment of technicians, leading to diverse evaluation results. This study focuses on the feature extraction and classification methods of surrounding rock images in a certain tunnel of the Central Yunnan Water Diversion Project by using image processing analysis and transfer learning. Rich surrounding rock images and the water conservancy tunnel data are collected, and then the surrounding rock is classified relatively accurately according to the code and expert guidance. By introducing the fractal theory, the complexity and irregularity of the spatial distribution of weak layers and joints on the surrounding rock surface are revealed effectively. Based on the analysis of changes in fractal dimension characteristic values, a classification method for surrounding rock based on the fractal theory is proposed. Combined with the quantified parameters of surrounding rock images and the strength data collected by rebound meters, a method for correcting the surrounding rock strength based on image analysis is proposed, which can effectively solve the error caused by the uneven distribution of rock masses in the traditional rebound meter strength values. After correction, more accurate strength characteristics can be obtained, which is conducive to the standardized classification of the surrounding rock. After studying the recognition of tunnel surrounding rock images with transfer learning, a model is constructed to achieve rapid classification of tunnel surrounding rock. This research provides support for the standardized classification of tunnel surrounding rock.