Real-time monitoring of evolutive structural damage from advanced sensing and simulation

In a digital world with the expansion of connected systems, a still open question is to design smart and autonomous mechanical structures able to perform online control of their health and take anticipated actions during service before downtime of failure occur. This is a critical need in several areas such as transport of energy, for more reliability but also more performance and durability of the employed structures (aircrafts, wind turbines, bridges.. .). Implementing such an advanced structural health monitoring (SHM) technology would permit optimized maintenance and capability to operate in degraded mode, managing the decrease of loading capabilities by adapting the operating plan. Tracking structural damage and predicting its evolution has been a perpetual engineering issue during the last decades. It was the topic of intensive research works, with both experimental and numerical advances. On the one hand, on-board sensing techniques nowadays permit accurate in situ measurements on displacement or strain fields and thus provide very rich experimental information on the internal damage state of materials (e.g. see the recent review [1] on distributed optic fiber sensing). On the other hand, sophisticated physics-based tools now permit to simulate damage phenomena with high-fidelity and give a relevant virtual image of the material state [2]. The scientific bottleneck for going from smart materials to smart structures is in the monitoring of large complex engineering systems. A shortcoming of data science alone is in the management and interpretation of sensor information with large noisy data sets, and in the prediction of the structural behavior involving localized multiscale and highly nonlinear damage phenomena. Moreover, from complex physics-based simulations alone, it is difficult to perform safe command within a changing environment and with real-time requirements in order to be reactive and avoid instabilities faced to damage evolutions.