Dynamic Interactional And Cooperative Network For Shield Machine

TL;DR

This paper introduces a dynamic interactional and cooperative network for shield machine control that integrates geological data and operational parameters, improving prediction accuracy and anomaly detection in tunneling operations.

Contribution

It develops models that incorporate geological information into shield machine monitoring, enhancing prediction and anomaly detection over existing methods.

Findings

Rate prediction R2 reaches 92.2%

MSE for rate prediction is 0.0064

Anomaly detection rate is 98.2%

Abstract

The shield machine (SM) is a complex mechanical device used for tunneling. However, the monitoring and deciding were mainly done by artificial experience during traditional construction, which brought some limitations, such as hidden mechanical failures, human operator error, and sensor anomalies. To deal with these challenges, many scholars have studied SM intelligent methods. Most of these methods only take SM into account but do not consider the SM operating environment. So, this paper discussed the relationship among SM, geological information, and control terminals. Then, according to the relationship, models were established for the control terminal, including SM rate prediction and SM anomaly detection. The experimental results show that compared with baseline models, the proposed models in this paper perform better. In the proposed model, the R2 and MSE of rate prediction can reach 92.2\%, and 0.0064 respectively. The abnormal detection rate of anomaly detection is up to 98.2\%.