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- Title
基于BIM 技术深度策划的隧道掘进智能控制技术研究.
- Authors
陈 利
- Abstract
In order to solve the problems of poor working environment, high safety risk and poor tunnel quality of manual operation of roadheader by traditional tunnel milling method, an intelligent control excavation method of tunnel excavation based on BIM and digital twin technology was proposed. Firstly, the threedimensional geological model of the tunnel was constructed by comprehensive physical detection on the ground, and the tunnel structure parameters, equipment model and measurement control component elements were imported to generate a BIM-based virtual tunnel face scene. Secondly, the motion path and trajectory of the cutting part during the cutting operation of the roadheader were deeply planned by using BIM and digital twin technology, the automatic cutting operation control program was designed, and the construction simulation collision and optimization were carried out to improve the accuracy of the simulation operation. The results show that, the method solves the problem of low level of intelligent and automatic construction of tunnel milling method, and realizes the automatic cutting operation of remote control operation. Using local area network and sensing technology, the problem of motion tracking and positioning of roadheader cutting unit in high dust and low illumination environment is solved, and the comprehensive perception of human-machineenvironment is realized. Through the sensing system to accurately locate, track and measure the position and posture of the tunnel boring machine and the cutting part, the remote monitoring and control of the tunnel boring machine is realized. By measurement and control, sensors and computer technology, the position of the cutting head can be accurately controlled to complete the tunnel section. It can automatically shape and cut and the control system accurately controls the movement of the cutting part to achieve a control accuracy of ±20 mm, which effectively improves the quality of tunnel forming.
- Publication
Railway Investigation & Surveying, 2024, Vol 50, Issue 3, p180
- ISSN
1672-7479
- Publication type
Article
- DOI
10.19630/j.cnki.tdkc.202310090001