TBM bad geological identification method based on intelligent driving model

Abstract

The invention provides a TBM unfavorable geology identification method based on an intelligent driving model to solve the problems of low identification degree and the like caused by the fact that a TBM driver judges geology according to experience of the TBM driver, and the method comprises the following steps: collecting TBM operation parameters reflecting an equipment operation state; preprocessing the obtained TBM operation parameters; according to the preprocessed TBM operation parameters, constructing TBM comprehensive parameters capable of comprehensively reflecting the interaction of the rock machine; extracting stable section mean value characteristics from the obtained TBM operation parameters and TBM comprehensive parameters, and a constructing TBM characteristic parameter matrix; according to the identified TBM normal stratum and the unfavorable geological section in the tunneling process, using an intelligent driving model to construct an unfavorable geological identification model between the TBM characteristic parameter matrix under the corresponding scalar section and whether there is unfavorable geology or not; and obtaining a new TBM characteristic parameter matrix for the newly obtained TBM operation parameters according to the method, and judging whether the current TBM enters bad geology or not on line through the established unfavorable geology identification model. The implementation method disclosed by the invention is simple and high in distinguishing degree.

Description

technical field [0001] The invention relates to the field of tunnel engineering TBM construction, in particular to a TBM bad geological identification method based on an intelligent driving model. Background technique [0002] Tunnel Boring Machine (TBM for short) is a large-scale key equipment specially used in underground tunnel engineering construction. In the actual tunnel excavation, the geological conditions of the rock mass are complex and changeable throughout the excavation process, especially when encountering some rock formations with well-developed joints or large-area broken zones, it is very easy to induce landslides, machine jams, etc. Accidents, which bring great danger to TBM construction. Therefore, poor geological identification and alarm are of vital significance to ensure the safety and reliability of TBM excavation. However, the traditional poor geological identification method requires TBM drivers to manually identify based on their own operating exp...

AI Technical Summary

Problems solved by technology

[0004] Aiming at the problem of low identification accuracy and poor robustness of the traditional method because the driver needs to judge the bad geology based on his own operating experience, the present invention proposes a TBM bad geology identification method based on an intelligent driving model, which can identify the current location of the TBM On-line real-time judgment of the geology, the implementation method is simple, the response speed is fast, the prediction accuracy is high, and it will not interfere with the normal tunneling of TBM

Images