SHM and smart structures have been an active research topic for several decades. However, achievements are still largely academic due to difficulties of transfer to real systems. Through the research work performed so far in the project, using unconventional approaches, we already achieved major advances compared to the state-of-the-part to envision this transfer.
First, contrary to many works that use artificial intelligence alone, we developed a consistent hybrid twin strategy that promotes physics-guided but also data-enriched methods for data assimilation and control, thus making benefit of all available knowledge stored in models and data. For instance, a priori physics knowledge is inserted in the neural network architectures and loss functions which are used, which leads to much higher performance. Moreover, all methods are bias-aware, which means that they naturally integrate uncertainties contained in models and data. Eventually, we introduced on-the-fly adaptive modeling with continuous evolution of the model complexity depending on the richness of experimental information, thus leading to a smart dynamical management of computing resources. All this enables to alleviate drawbacks of purely data-based approaches in terms of interpretability, prediction capability, and robustness. It also allows for high efficiency and accuracy without considering computationally demanding models or large amounts of data, which are limited as difficult and expensive to obtain in practical engineering applications.
Related to experimental aspects, we did not work only with synthetic data, but also with real data coming from optic fiber sensing, successfully inferring noisy heterogeneous sequential measurements streams. Such an advanced high-resolution sensing technology is increasingly used in industry, it was thus important to consider it to show transfer capabilities.
Eventually, we addressed both data assimilation and command synthesis in parallel, which is almost never done in the literature as it refers to separate scientific communities. In the project, we develop multidisciplinary interactions and we mix a variety of tools and methods which come from these separate communities (computer science, mechanical engineering, data science).
It is expected that the overall methodology be validated on a representative and original lab experiment, with real-time damage tracking and monitoring to preserve the integrity of a composite structure under controlled multi-axial loading. It will use the purchased experimental equipment (DOFS interrogator & Stewart platform) for online adaptation of the operational plan to prevent the structure from operating outside a safety regime. This proof-of-concept will show the interest of the project outcomes for the design of the next-generation of SHM systems.
