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Analysis of institutional authors

Del-Río-Velilla, DanielCorresponding AuthorPedraza, AndrésAuthorFernandez-Lopez, AntonioAuthor

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September 1, 2024
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Impact localization in composite structures with Deep Neural Networks

Publicated to: STRUCTURAL HEALTH MONITORING-AN INTERNATIONAL JOURNAL. 24 (6): 3907-3920 - 2025-11-01 24(6), DOI: 10.1177/14759217241270946

Authors:

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

Univ Politecn Madrid, Madrid, Spain - Author

Abstract

This research looks at the application of Deep Neural Networks (DNNs) for low-energy impact localization in composite structures, a key aspect of structural health monitoring in the aerospace sector. The methodology used in this study involves the generation of a consistent impact dataset using an autonomous impact machine, followed by meticulous data processing. The training of the DNN models was focused on minimizing the Euclidean distance between the predicted and actual impact positions employing custom loss functions. This study yielded several significant findings. First, it confirmed the feasibility of using DNNs for effective impact localization in complex composite structures, although with varying degrees of accuracy across different impact locations but with an average error of the same order as the labeling error. Second, it was observed that the performance of the models was considerably influenced by structural features, such as the presence of stringers and the placement of sensors. The architecture demonstrated consistent performance across multiple trained models, indicating their robustness and potential for generalization. The implications of these findings for structural health monitoring are substantial, suggesting that DNNs can be a valuable tool for early damage detection in composite structures.
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Keywords

Aerospace sectorsArtificial intelligencArtificial intelligenceCfrpComposite structuresComposites structuresDamageDeep neural networkFatiguHealth monitoringImpact locationImpact locationsImpact machinesLocalisationLocation estimationLow-energy impactNeural-networksShmStringersStructural health

Quality index

Bibliometric impact. Analysis of the contribution and dissemination channel

The work has been published in the journal STRUCTURAL HEALTH MONITORING-AN INTERNATIONAL JOURNAL 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 16/179, thus managing to position itself as a Q1 (Primer Cuartil), in the category Engineering, Multidisciplinary. Notably, the journal is positioned above the 90th percentile.

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 2026-04-27:

  • WoS: 1
  • Scopus: 2
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Impact and social visibility

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:

  • Assignment of a Handle/URN as an identifier within the deposit in the Institutional Repository: https://oa.upm.es/93411/

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: 37
  • Downloads: 17
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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 (RIO VELILLA, DANIEL DEL) and Last Author (FERNANDEZ LOPEZ, ANTONIO).

the author responsible for correspondence tasks has been RIO VELILLA, DANIEL DEL.

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Awards linked to the item

The author(s) disclosed receipt of the following financial sup-port for the research, authorship, and/or publication of this article: This project has received funding from the National Research Program Retos de la Sociedad under the Project STARGATE: Desarrollo de un sistema de monitorizacio nestructural basado en un microinterrogador y redes neuronales (reference PID2019-105293RB-C21).
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