Combined Structural Health and Traffic Monitoring using Fiber Optic Distributed Acoustic Sensing

At present, study of the integration of structural monitoring and traffic monitoring is presently underserved in researchers. To our knowledge, no researcher has used acoustic emission measurements obtained from distributed optical fiber sensors to simultaneously monitor bridge health and traffic conditions from a single measurement while simultaneously incorporating both finite element modelling and an artificial neural network for data analysis. The combination of all of these elements into a single system would represent a path forward to an all-in-one bridge monitoring system. By combining traffic monitoring and health monitoring from a single data source, asset owners would stand to benefit from both increased data and reduced monitoring costs. If successfully executed, such a system would also have potential for future expansion to accommodate additional data collection and analysis tasks, as the structural dynamics model and artificial intelligence algorithm can be easily amended or expanded to accommodate such tasks in a variety of bridge and traffic conditions. This level of expandability and customizability greatly increases the future commercial viability and practical applicability. Accordingly, it is recommended that the viability of the proposed system be more thoroughly analyzed. Objectives: The proposed research seeks to investigate the viability of the use of Distributed Optical Fiber Sensors (DOFS) that measure acoustic emission (AE) for use as a combined bridge health monitoring and vehicle classification tool. An artificial neural network will be developed to process DOFS AE data to classify vehicle loading and categorize results. Categorized results will be compared against results generated by a structural dynamics FEM to serve as a method of determining the health of the structure.