PSI - Issue 80

ScienceDirect Structural Integrity Procedia 00 (2022) 000 – 000 Structural Integrity Procedia 00 (2022) 000 – 000 Available online at www.sciencedirect.com Available online at www.sciencedirect.com ScienceDirect Available online at www.sciencedirect.com ScienceDirect

www.elsevier.com/locate/procedia www.elsevier.com/locate/procedia

Procedia Structural Integrity 80 (2026) 403–410

© 2025 The Authors. Published by ELSEVIER B.V. This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0) Peer-review under responsibility of Ferri Aliabadi Abstract Ultrasonic Guided Waves (UGWs) are widely used in Structural Health Monitoring (SHM) systems for their ability to inspect large areas with minimal sensor intrusiveness. However, extracting reliable damage information from UGW signals remains challenging, especially in complex composite structures. This study presents a data-driven methodology for classifying low-velocity impact damage in Carbon Fiber Reinforced Polymer (CFRP) panels using UGWs and unsupervised clustering. Ten CFRP plates, each equipped with six piezoelectric transducers, were subjected to controlled 15 J impacts at their geometric centres. For each panel, UGWs were acquired in both pristine and damaged conditions through a sequential pitch-catch configuration. A set of 32 scalar features was extracted from each signal, and all possible pairwise and triplet combinations of features were explored to evaluate their effectiveness in damage classification. Unsupervised clustering algorithms were applied to assess the separability of healthy and damaged states, with performance evaluated via silhouette score, purity, and cluster balance. The results highlighted the ability of specific feature combinations to consistently differentiate impact damage without prior labelling, offering a lightweight and interpretable approach for structural diagnostics. This work demonstrates the viability of combining UGWs with unsupervised learning for effective and scalable SHM of composite structures. © 2023 The Authors. Published by ELSEVIER B.V. This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0) Peer-review under responsibility of Professor Ferri Aliabadi Keywords: SHM; UGW; Damage identification. Fracture, Damage and Structural Health Monitoring Role of Ultrasonic Guided Wave-Based SHM in Detecting Low Velocity Impact Damage in CFRP Laminates Alessandro De Luca a *, Giuseppe Lamanna a , Luciano Pianese a , Antonio Polverino a , Nima Rezazadeh a , Antonio Aversano a a Department of Engineering, University of Campania “L. Vanvitelli”, 81031, Via Roma 29, Aversa, Italy Abstract Ultrasonic Guided Waves (UGWs) are widely used in Structural Health Monitoring (SHM) systems for their ability to inspect large areas with minimal sensor intrusiveness. However, extracting reliable damage information from UGW signals remains challenging, especially in complex composite structures. This study presents a data-driven methodology for classifying low-velocity impact damage in Carbon Fiber Reinforced Polymer (CFRP) panels using UGWs and unsupervised clustering. Ten CFRP plates, each equipped with six piezoelectric transducers, were subjected to controlled 15 J impacts at their geometric centres. For each panel, UGWs were acquired in both pristine and damaged conditions through a sequential pitch-catch configuration. A set of 32 scalar features was extracted from each signal, and all possible pairwise and triplet combinations of features were explored to evaluate their effectiveness in damage classification. Unsupervised clustering algorithms were applied to assess the separability of healthy and damaged states, with performance evaluated via silhouette score, purity, and cluster balance. The results highlighted the ability of specific feature combinations to consistently differentiate impact damage without prior labelling, offering a lightweight and interpretable approach for structural diagnostics. This work demonstrates the viability of combining UGWs with unsupervised learning for effective and scalable SHM of composite structures. © 2023 The Authors. Published by ELSEVIER B.V. This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0) Peer-review under responsibility of Professor Ferri Aliabadi Keywords: SHM; UGW; Damage identification. Fracture, Damage and Structural Health Monitoring Role of Ultrasonic Guided Wave-Based SHM in Detecting Low Velocity Impact Damage in CFRP Laminates Alessandro De Luca a *, Giuseppe Lamanna a , Luciano Pianese a , Antonio Polverino a , Nima Rezazadeh a , Antonio Aversano a a Department of Engineering, University of Campania “L. Vanvitelli”, 81031, Via Roma 29, Aversa, Italy

1. Introduction 1. Introduction

Structural Health Monitoring (SHM) plays a crucial role in ensuring the safety, reliability, and durability of Structural Health Monitoring (SHM) plays a crucial role in ensuring the safety, reliability, and durability of

* Corresponding author. E-mail address: alessandro.deluca@unicampania.it * Corresponding author. E-mail address: alessandro.deluca@unicampania.it

2452-3216 © 2023 The Authors. Published by ELSEVIER B.V. This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0) Peer-review under responsibility of Professor Ferri Aliabadi 2452-3216 © 2023 The Authors. Published by ELSEVIER B.V. This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0) Peer-review under responsibility of Professor Ferri Aliabadi

2452-3216 © 2025 The Authors. Published by ELSEVIER B.V. This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0) Peer-review under responsibility of Ferri Aliabadi 10.1016/j.prostr.2026.02.038