AI-Based Impact Location in Structural Health Monitoring for Aerospace Application Evaluation Using Explainable Artificial Intelligence Techniques

Due to the nature of composites, the ability to accurately locate low-energy impacts on structures is crucial for Structural Health Monitoring (SHM) in the aerospace sector. For this purpose, several techniques have been developed in the past, and, among them, Artificial Intelligence (AI) has demonstrated promising results with high performance. The non-linear behavior of AI-based solutions has made them able to withstand scenarios where complex structures and different impact configurations have been introduced, making accurate location predictions. However, the black-box nature of AI poses a challenge in the aerospace field, where reliability, trustworthiness, and validation capability are paramount. To overcome this problem, Explainable Artificial Intelligence (XAI) techniques emerge as a solution, enhancing model transparency, trust, and validation. This research presents a case study: a previously trained Impact-Locator-AI model is, initially, demonstrating a promising location accuracy; however, its behavior in real-life scenarios is unknown, and before embedding it in an aerospace structure as an SHM system its reliability must be tested. By applying XAI methodologies, the Impact-Locator-AI model can be critically evaluated to assess its reliability and potential suitability for aerospace applications, while also laying the groundwork for future research at the intersection of XAI and impact location in SHM.