Dynamic Interactional And Cooperative Network For Shield Machine
This addresses limitations in traditional shield machine construction, such as hidden failures and human errors, for tunneling engineering, but it is incremental as it builds on existing intelligent methods by incorporating environmental factors.
The paper tackled the problem of monitoring and decision-making in shield machine tunneling by considering the relationship among the machine, geological information, and control terminals, resulting in models for rate prediction and anomaly detection with R2 of 92.2%, MSE of 0.0064, and detection rate of 98.2%.
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\%.