Research on Obtaining Shield-Tail Clearance Based on Machine Vision Measurement and Intelligent Prediction with Neural Networks

Abstract

This article presents a novel gadget that utilizes machine vision technology to automatically measure the clearance of shield tails in real-time. The device uses image processing to pull out important details from pictures, which makes it possible to precisely find the inner zero point of the shield shell and the edge of the pipe segment, even when lighting is poor. Through the use of computer algorithms and the creation of a dynamic scale, the device gives accurate measurements of the distance between the shield and the tail right away. Consequently, this technological solution successfully addresses the challenge of measuring shield-tail clearance without requiring physical touch. Furthermore, based on data analytics and correlation screening of multi-source construction and geological parameters, a forecasting model centered on the Long Short-Term Memory (LSTM) neural network is proposed. Through integration of optimization techniques including genetic algorithms (GA) and particle swarm optimization (PSO), a composite forecasting framework is established. This framework incorporates generative adversarial networks and k-fold cross-validation methods to effectively mitigate model overfitting and enhance robustness. Finally, engineering simulations are performed using measured data from the Beijing Metro project to validate the accuracy of the automatic measurement equipment. Moreover, the intelligent prediction of shield-tail clearance is achieved by utilizing a combination prediction model framework that incorporates both shield construction parameters and geological parameters.