PSI - Issue 77

ScienceDirect Structural Integrity Procedia 00 (2026) 000–000 Structural Integrity Procedia 00 (2026) 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 77 (2026) 111–118

© 2026 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 ICSI organizers Abstract The initiation and progression of damage in composite structures is of great importance for the design, manufacturing and structural health monitoring (SHM) of an increasingly large variety of structures. Acoustic Emission (AE) monitoring has emerged as a highly effective technique for structural health monitoring (SHM) in composite materials, able to capture crack formation and propagation. However, a current challenge of this method is the segregation of signals for the identification of different failure modes. This limitation can be approached by distinguishing the frequency spectra of individual damage events using the AE raw signals. In this study, a finite element model is developed to generate AE signals in a glass fibre composite. Damage evolution modelled with Hashin failure criteria is used for damage analysis of a 24-ply uniaxial glass-epoxy notched square specimen, used for crack initiation under linear tensile loading. The structure is modelled with 2D quadratic shell elements, and the dynamic explicit solver is used for analysis. AE signals are recorded independently for distinct damage events, such as matrix breakage and fibre cracking, and the propagation of AE waves caused by crack growth is captured in time-domain plots. Then, frequency content is extracted using Fast Fourier Transform (FFT). Raw signals are studied for the classification of AE signals and the identification of damage modes according to the frequency range and patterns. The results demonstrate that each damage mechanism can be characterised by its AE signature. The proposed method provides a pathway to generate AE datasets to train artificial intelligence models to identify damage in E-glass composite structures under deformation and failure. © 2026 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 ICSI organizers Keywords: Finite Element Modelling; Acoustic Emissions; Structural Health Monitoring; Support Vector Machine; Damage Mode Classification acoustic emissions and finite element modelling Muhammad Jahanzeb Zia a, *, Yu Zhang a , Christopher M. Harvey a , Konstantinos P. Baxevanakis b a Department of Aeronautical and Automotive Engineering, Loughborough University, Leicestershire, LE11 3TU, UK b Wolfson School of Mechanical, Electrical and Manufacturing Engineering, Loughborough University, Leicestershire, LE11 3TU, UK Abstract The initiation and progression of damage in composite structures is of great importance for the design, manufacturing and structural health monitoring (SHM) of an increasingly large variety of structures. Acoustic Emission (AE) monitoring has emerged as a highly effective technique for structural health monitoring (SHM) in composite materials, able to capture crack formation and propagation. However, a current challenge of this method is the segregation of signals for the identification of different failure modes. This limitation can be approached by distinguishing the frequency spectra of individual damage events using the AE raw signals. In this study, a finite element model is developed to generate AE signals in a glass fibre composite. Damage evolution modelled with Hashin failure criteria is used for damage analysis of a 24-ply uniaxial glass-epoxy notched square specimen, used for crack initiation under linear tensile loading. The structure is modelled with 2D quadratic shell elements, and the dynamic explicit solver is used for analysis. AE signals are recorded independently for distinct damage events, such as matrix breakage and fibre cracking, and the propagation of AE waves caused by crack growth is captured in time-domain plots. Then, frequency content is extracted using Fast Fourier Transform (FFT). Raw signals are studied for the classification of AE signals and the identification of damage modes according to the frequency range and patterns. The results demonstrate that each damage mechanism can be characterised by its AE signature. The proposed method provides a pathway to generate AE datasets to train artificial intelligence models to identify damage in E-glass composite structures under deformation and failure. © 2026 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 ICSI organizers Keywords: Finite Element Modelling; Acoustic Emissions; Structural Health Monitoring; Support Vector Machine; Damage Mode Classification International Conference on Structural Integrity Failure mode identification in E-glass laminates under tension using acoustic emissions and finite element modelling Muhammad Jahanzeb Zia a, *, Yu Zhang a , Christopher M. Harvey a , Konstantinos P. Baxevanakis b a Department of Aeronautical and Automotive Engineering, Loughborough University, Leicestershire, LE11 3TU, UK b Wolfson School of Mechanical, Electrical and Manufacturing Engineering, Loughborough University, Leicestershire, LE11 3TU, UK International Conference on Structural Integrity Failure mode identification in E-glass laminates under tension using

* Corresponding author. Muhammad Jahanzeb Zia Tel.: +44 7777 327531. E-mail address: m.j.zia@lboro.ac.uk * Corresponding author. Muhammad Jahanzeb Zia Tel.: +44 7777 327531. E-mail address: m.j.zia@lboro.ac.uk

2452-3216 © 2026 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 ICSI organizers 2452-3216 © 2026 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 ICSI organizers

2452-3216 © 2026 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 ICSI organizers 10.1016/j.prostr.2026.01.016