Clogging Risk Early Warning for Slurry Shield Tunneling in Mixed Mudstone–Gravel Ground: A Real-Time Self-Updating Machine Learning Approach

Abstract

Clogging constitutes a significant obstacle to shield tunneling in mudstone soils. Previous research has focused on investigating the influence of soils and slurry properties on clogging, although little attention has been paid to the impact of tunneling parameters on clogging, and particularly early clogging warning during tunneling. This paper contributes to developing a real-time clogging early-warning approach, based on a self-updating machine learning method. The clogging judgment criteria are based on the statistical characteristics of whole-ring tunneling parameters. The paper proposes the use of random forest (RF) for a real-time self-updating early warning strategy for clogging. The performance of this approach is illustrated through its application to a slurry-pressure-balanced shield tunneling construction of Nanning metro line 1. Results show that the RF-based approach can predict clogging during a ring construction with only four minutes of tunneling data, with an accuracy of 95%. The RF model provided the best performance compared with the other machine learning methods. Furthermore, the RF model can realize an accurate clogging prediction in one ring, using less tunneling data with the self-updating mechanism.

Anomaly Detection and Identification Method for Shield Tunneling Based on Energy Consumption Perspective

“…In terms of anomaly detection model design, traditional anomaly detection research usually relies on supervised models, which need labeled data. Zhai et al [9] proposed a random forest-based classification method that needs labeled operational data as input and predicts the abnormal states in the shield tunneling process. However, anomalies in the shield tunneling process are diverse and constitute a minority of the entire dataset, making it challenging to acquire labeled anomaly samples to train the models.…”