Resumen
Various abnormal scenarios might occur during the shield tunneling process, which have an impact on construction efficiency and safety. Existing research on shield tunneling construction anomaly detection typically designs models based on the characteristics of a specific anomaly, so the scenarios of anomalies that can be detected are limited. Therefore, the research objective of this article is to establish an accurate anomaly detection model with generalization and identification capabilities on multiple types of abnormal scenarios. Inspired by energy dissipation theory, this paper innovatively detects various anomalies in the shield tunneling process from the perspective of energy consumption and designs the AD_SI model (Anomaly Detection and Scenario Identification model of shield tunneling) based on machine learning. The AD_SI model first monitors the shield machine?s energy consumption status based on the VAE-LSTM (Variational Autoencoder?Long Short-Term Memory) algorithm with a dynamic threshold, thereby detecting abnormal sections. Secondly, the AD_SI model uses the correlation of construction parameters to represent different known scenarios and further clarifies scenarios of the abnormal sections, thus achieving anomaly identification. The application of the AD_SI model in a shield tunneling construction project demonstrates its capability to accurately detect and identify different anomalies, with a recall value exceeding 0.9 and F1 exceeding 0.8, thereby providing guidance for accurately detecting multiple types anomaly scenarios in practical applications.