Smart Anomaly Detection on Water Infrastructures through Distributed Fiber Optic Sensors and Sensor Fusion: a Case Study

Today, water infrastructures must face new challenges that affect both their design and their management. Many of these strategic infrastructures have reached or are approaching the lower limit of their lifespan, and this mass aging increasingly requires widespread and efficient monitoring of their functionality. In addition, climate change is leading to new operational scenarios for these infrastructures. To address these issues, this article presents a novel Structural Health Monitoring approach for this kind of infrastructure based on Distributed Fiber Optic Sensors (DFOS) and Data Fusion. We field tested this concept by installing a DFOS-based system on a reinforced concrete pipe bridge to monitor the strain and temperature variations of its structural elements. In four years, the system collected approximately 20 million operational states, providing a suitable dataset to perform data-driven anomaly detection algorithms. The high accuracy and resolution of DFOS allow for high performance in the unsupervised detection of operational anomalies using Machine Learning-based algorithms. By applying the concept of sensor fusion enabled by the Internet of Things and the availability of open data, we have also demonstrated that it is also possible to classify strain anomalies according to the environmental factors that triggered them. This is a strategic tool as it makes it possible to distinguish in real time which anomalies require attention as they are not strictly linked to the variability of environmental or operational factors. At the same time, continuous monitoring of operational anomalies caused by environmental factors provides a useful knowledge framework for studying adaptation strategies to the effects of climate change on water infrastructures.