Risks and opportunities of using fibre optic sensors for long term infrastructure health monitoring systems in an 18 year old installation
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AuthorRobles Urquijo, Ignacio; Quintela Incera, Antonio; Van Vaerenbergh, Steven; Inaudi, Daniele; López Higuera, José Miguel
Attribution 4.0 International
International Conference on Smart Infrastructure and Construction (ICSIC), Cambridge, London, 2019, 623-630
Uncertainty about the survivability and technological evolution presents its risks when designing the sensors to use on infrastructure health monitoring systems. Fibre optic methods have been available for some time, and are reaching the technological maturity required to provide reliable long-term structural health monitoring solutions. By embedding fibre optic sensors in the structures at construction time, the engineers are able to detect deviations from the structure’s original design and expected dynamics. However, due to the long-term infrastructure’s life span and the relatively new fibre optic techniques, there are very few historical examples to analyse and use as proof for the adequacy of such techniques to the infrastructure’s life-cycle. This study explores the risks and opportunities associated with these technologies through a real case of one of the first Fibre Bragg Grating (FBG) sensorized highway bridge installed in Spain in the year 2000, the «Las Navas» bridge at the A-8 «Autovía del Cantábrico» highway in the north of Spain. The survivability of the fibre optic sensors, after eighteen years of embedded exposure, is compared to the traditional strain gauges solutions and the maintenance challenges they face. And, most importantly, the exciting new opportunities that the new measurement units can offer, are analysed, evaluating the re-purpose capacity of the health monitoring systems fibre optic sensors as sustainable real time operational monitoring systems. The originally designed structural sensors are proved to be useful also to detect, count and classify operational traffic using the infrastructure, by applying machine learning techniques that add another benefit of the use of these type of measurement sensors into the infrastructure’s world.