AI-Based Impact Location in SHM for Aerospace Applications Evaluation Using eXplainable Artificial Intelligence Techniques
Andres Fernando Pedraza, Daniel del-Río-Velilla, Antonio Fernández-López · Preprints.org · 2025
Due to the nature of composites, the ability to accurately locate low-energy impacts 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 behaviour of AI-based solutions has made them able to withstand scenarios where complex structures and different impact configuration 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 places a previously trained Impact-Locator-AI under the spotlight, revealing whether it is truly reliable and worthy of application in aerospace industry.