# Intelligent Identification of Micro-NPR Bolt Shear Deformation Based on Modular Convolutional Neural Network

## TL;DR

This paper introduces a new method using a modular convolutional neural network to detect shear deformation in micro-NPR bolts used for slope support.

## Contribution

The novel approach combines stress wave nondestructive testing with a modular CNN to improve shear deformation detection accuracy in micro-NPR bolts.

## Key findings

- Stress wave nondestructive detection combined with modular CNN improves shear deformation identification.
- Integration of shear angle and location sub-modules enhances detection accuracy.
- The method is applicable for quality inspection in engineering support systems.

## Abstract

As an important means of reinforcement and support, the bolt can effectively resolve the problem of slope instability. Micro-Negative Poisson Ratio (Micro-NPR) bolts are superior to conventional bolts in mitigating large deformations caused by geological shifts. A large number of bolt anchoring systems require non-destructive testing technology for quality inspection. This technology utilizes time-domain signal characteristics to detect internal defects in the bolt anchoring systems of support engineering. The combination of stress wave nondestructive detection technology and modular convolutional neural network method can identify the shear deformation in the case of the anchor slope support. Integrating the identification results of both the shear angle and shear location sub-modules improves the accuracy of detecting shear deformation in micro-NPR bolt anchoring system, which will be of great assistance in our future engineering applications.