Armijo, A.; Zamora-Sánchez, D. Integration of Railway Bridge Structural Health Monitoring into the Internet of Things with a Digital Twin: A Case Study. Sensors2024, 24, 2115.
Armijo, A.; Zamora-Sánchez, D. Integration of Railway Bridge Structural Health Monitoring into the Internet of Things with a Digital Twin: A Case Study. Sensors 2024, 24, 2115.
Armijo, A.; Zamora-Sánchez, D. Integration of Railway Bridge Structural Health Monitoring into the Internet of Things with a Digital Twin: A Case Study. Sensors2024, 24, 2115.
Armijo, A.; Zamora-Sánchez, D. Integration of Railway Bridge Structural Health Monitoring into the Internet of Things with a Digital Twin: A Case Study. Sensors 2024, 24, 2115.
Abstract
Structural health monitoring (SHM) is critical for ensuring the safety of infrastructures like bridges. This article presents a digital twin solution for SHM of railway bridges using low-cost wireless accelerometers and Machine Learning (ML). The system architecture combines edge on-premises computing and cloud analytics to enable efficient real-time monitoring and complete storage of relevant time-history datasets. After train crossings, accelerometers stream raw vibration data, which is processed in the frequency domain and analyzed via machine learning to detect anomalies indicating potential structural issues. The digital twin approach is demonstrated on an in-service railway bridge, where vibration data was collected over two years under normal operating conditions. By learning allowable ranges for vibration patterns, the digital twin model identifies abnormal spectral peaks, suggesting changes in structural integrity. The long-term pilot proves that this affordable SHM system can provide automated and real-time warning of bridge damage and also supports the use of in-house designed sensors of lower-cost and edge computing capabilities than those used in the demonstration. The successful on-premises-cloud hybrid implementation provides a cost effective and scalable model for expanding monitoring to thousands of railway bridges, democratizing SHM to improve safety by avoiding catastrophic failures.
Keywords
Structural Health Monitoring (SHM); Railway bridges; Digital Twin (DT); Machine Learning (ML); MLOps; AutoML; Low-cost sensors; Anomaly detection; Cloud analytics; MQTT; MEMS accelerometers; Vibration-based monitoring; Wireless Sensor Networks (WSN); On-premises computing; Cloud computing; Hybrid computing; Building Information Modelling (BIM)
Subject
Engineering, Architecture, Building and Construction
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
