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Analysis of institutional authors

Pedraza, AndrésCorresponding AuthorDel-Río-Velilla, DanielAuthorFernandez-Lopez, AntonioAuthor

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June 3, 2025
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AI-Based Impact Location in Structural Health Monitoring for Aerospace Application Evaluation Using Explainable Artificial Intelligence Techniques

Publicated to: Electronics. 14 (10): 1975- - 2025-05-12 14(10), DOI: 10.3390/electronics14101975

Authors:

Pedraza, Andres; del-Rio-Velilla, Daniel; Fernandez-Lopez, Antonio
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Affiliations

Univ Politecn Madrid, Inst Ignacio Riva IDR, Madrid 28040, Spain - Author

Abstract

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.
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Keywords

AerospacAerospaceArtificial intelligence techniquesCfrpCompositeDamagEmbedded aiEmbedded artificial intelligenceExplainable aiExplainable artificial intelligenceHealth monitoringImpact locationsIntelligence modelsLow-energy impactNeural networkNeural-networksResearch aircraftShmStructural health

Quality index

Bibliometric impact. Analysis of the contribution and dissemination channel

The work has been published in the journal Electronics due to its progression and the good impact it has achieved in recent years, according to the agency WoS (JCR), it has become a reference in its field. In the year of publication of the work, 2025, it was in position 174/368, thus managing to position itself as a Q2 (Segundo Cuartil), in the category Engineering, Electrical & Electronic. Notably, the journal is positioned en el Cuartil Q2 para la agencia Scopus (SJR) en la categoría Electrical and Electronic Engineering.

Independientemente del impacto esperado determinado por el canal de difusión, es importante destacar el impacto real observado de la propia aportación.

Según las diferentes agencias de indexación, el número de citas acumuladas por esta publicación hasta la fecha 2026-04-25:

  • WoS: 2
  • Scopus: 2
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Impact and social visibility

From the perspective of influence or social adoption, and based on metrics associated with mentions and interactions provided by agencies specializing in calculating the so-called "Alternative or Social Metrics," we can highlight as of 2026-04-25:

  • The use of this contribution in bookmarks, code forks, additions to favorite lists for recurrent reading, as well as general views, indicates that someone is using the publication as a basis for their current work. This may be a notable indicator of future more formal and academic citations. This claim is supported by the result of the "Capture" indicator, which yields a total of: 12 (PlumX).

With a more dissemination-oriented intent and targeting more general audiences, we can observe other more global scores such as:

    It is essential to present evidence supporting full alignment with institutional principles and guidelines on Open Science and the Conservation and Dissemination of Intellectual Heritage. A clear example of this is:

    • The work has been submitted to a journal whose editorial policy allows open Open Access publication.
    • Assignment of a Handle/URN as an identifier within the deposit in the Institutional Repository: https://oa.upm.es/93589/

    As a result of the publication of the work in the institutional repository, statistical usage data has been obtained that reflects its impact. In terms of dissemination, we can state that, as of

    • Views: 30
    • Downloads: 13
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    Leadership analysis of institutional authors

    There is a significant leadership presence as some of the institution’s authors appear as the first or last signer, detailed as follows: First Author (PEDRAZA RODRÍGUEZ, ANDRÉS) and Last Author (FERNANDEZ LOPEZ, ANTONIO).

    the author responsible for correspondence tasks has been PEDRAZA RODRÍGUEZ, ANDRÉS.

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    Project objectives

    La aportación persigue los siguientes objetivos: analizar la capacidad de localizar impactos de baja energía en estructuras compuestas mediante inteligencia artificial en el ámbito aeroespacial; evaluar el desempeño y precisión del modelo Impact-Locator-AI previamente entrenado; determinar la fiabilidad y validez del modelo en escenarios reales mediante técnicas de inteligencia artificial explicable (XAI); caracterizar la transparencia y confianza del modelo para su aplicación en sistemas de monitorización estructural; y establecer las bases para futuras investigaciones en la intersección entre XAI y localización de impactos en monitorización estructural.
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    Most relevant results

    El estudio aborda la localización de impactos de baja energía en estructuras compuestas mediante inteligencia artificial (IA) para aplicaciones aeroespaciales. Los resultados más relevantes son: 1) el modelo Impact-Locator-AI entrenado mostró alta precisión en la localización de impactos; 2) la capacidad del modelo para manejar comportamientos no lineales permitió predecir impactos en estructuras complejas y configuraciones diversas; 3) la aplicación de técnicas de Inteligencia Artificial Explicable (XAI) facilitó la evaluación crítica de la fiabilidad del modelo; 4) se mejoró la transparencia y confianza en el sistema, aspectos clave para su validación en el sector aeroespacial; 5) se establecieron bases para futuras investigaciones en la intersección de XAI y monitorización estructural.
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    Awards linked to the item

    This project has received funding from the National Research Program Retos de la Sociedad under the Project STARGATE: Desarrollo de un sistema de monitorizacion estructural basado en un microinterrogador y redes neuronales (reference PID2019-105293RB-C21).
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