Abstract Description: Distributed Fiber Optic Sensing (DFOS) transforms fiber optic cables into dense arrays of sensors, enabling continuous monitoring of critical infrastructure systems. This technology is especially advantageous for infrastructure that is buried or spans long distances, such as underground water pipelines and road networks. Monitoring these systems is crucial due to their susceptibility to corrosion, wear, and external disturbances, yet presents significant challenges due to their inaccessibility and the need to detect early signs of failure by monitoring their behavior along their entire length.
At the UC Berkeley Center for Smart Infrastructure (CSI), we employ a comprehensive approach that combines lab tests, field deployments, numerical modeling, and the development of machine learning (ML) methods to advance the monitoring and management of civil infrastructure. Our objectives include investigating the behavior of infrastructure systems under both controlled and real-world conditions, examining infrastructure performance in response to various stressors, and building a DFOS data framework to support real-time, data-driven monitoring and management. By collaborating with municipal and utility partners, we ensure that our methods are robust and applicable to diverse infrastructure scenarios, enabling the prediction of potential failures, optimization of monitoring strategies, and supporting decision-making in predicting life expectancy and planning maintenance and rehabilitation.
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