PSI - Issue 80

Available online at www.sciencedirect.com Structural Integrity Procedia 00 (2025) 000–000 Available online at www.sciencedirect.com ScienceDirect Structural Integrity Procedia 00 (2025) 000–000 Available online at www.sciencedirect.com ScienceDirect

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

ScienceDirect

Procedia Structural Integrity 80 (2026) 77–92

© 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 © 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 Professor Ferri Aliabadi Abstract A probabilistic digital twin framework is developed for fatigue prognosis of aerospace structures by fusing passive sensing based on higher harmonic analysis, historical test data, and physics-based crack-growth models within a Dynamic Bayesian Network (DBN). Three progressive data assimilation schemes are evaluated: (i) purely online sensing, (ii) hybrid transition from historical to-online data, and (iii) conditional progressive fusion of both sources. Transfer learning leverages offline experiments to constrain priors, enabling rapid convergence of material parameter posteriors and effective initial flaw size estimation while reducing the volume of online observations. Experimental validation on a metallic plate with a central hole under constant-amplitude cyclic loading demonstrates accurate whole-life prognosis, with DBN predictions closely tracking observed crack evolution while quantifying measurement, material, and model uncertainties via calibrated credible intervals. Benchmarking against Gaussian Process Regression (GPR) highlights the DBN’s superior sequential uncertainty quantification, while GPR offers computational efficiency for near-real-time extrapolation. The hybrid DBN framework thus provides robust, uncertainty-aware life prognosis, advancing digital twin methodologies for aerospace applications. © 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 Professor Ferri Aliabadi Fracture, Damage and Structural Health Monitoring Digital Twin Framework for In-Service Crack Monitoring and Fatigue Prognosis in Metallic Aerospace Structures Sadjad Naderi*, Yuhang Pan, Ilias N Giannakeas, Zahra Sharif Khodaei*, Ferri M.H. Aliabadi* Department of Aeronatics, Imperial College London, South Kensington Campus, City and Guilds Building, Exhibition Road, SW7 2AZ, London, UK Abstract A probabilistic digital twin framework is developed for fatigue prognosis of aerospace structures by fusing passive sensing based on higher harmonic analysis, historical test data, and physics-based crack-growth models within a Dynamic Bayesian Network (DBN). Three progressive data assimilation schemes are evaluated: (i) purely online sensing, (ii) hybrid transition from historical to-online data, and (iii) conditional progressive fusion of both sources. Transfer learning leverages offline experiments to constrain priors, enabling rapid convergence of material parameter posteriors and effective initial flaw size estimation while reducing the volume of online observations. Experimental validation on a metallic plate with a central hole under constant-amplitude cyclic loading demonstrates accurate whole-life prognosis, with DBN predictions closely tracking observed crack evolution while quantifying measurement, material, and model uncertainties via calibrated credible intervals. Benchmarking against Gaussian Process Regression (GPR) highlights the DBN’s superior sequential uncertainty quantification, while GPR offers computational efficiency for near-real-time extrapolation. The hybrid DBN framework thus provides robust, uncertainty-aware life prognosis, advancing digital twin methodologies for aerospace applications. Fracture, Damage and Structural Health Monitoring Digital Twin Framework for In-Service Crack Monitoring and Fatigue Prognosis in Metallic Aerospace Structures Sadjad Naderi*, Yuhang Pan, Ilias N Giannakeas, Zahra Sharif Khodaei*, Ferri M.H. Aliabadi* Department of Aeronatics, Imperial College London, South Kensington Campus, City and Guilds Building, Exhibition Road, SW7 2AZ, London, UK

Keywords: Digital Twin; Prognosis; Fatigue; Dynamic Bayesian Network; EIFS Keywords: Digital Twin; Prognosis; Fatigue; Dynamic Bayesian Network; EIFS

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 * Corresponding author. E-mail address: sadjad.naderi@imperial.ac.uk (S. Naderi), z.sharif-khodaei@imperial.ac.uk (Z. Sharif Khodaei), m.h.aliabadi@imperial.ac.uk (F.M.H. 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 * Corresponding author. E-mail address: sadjad.naderi@imperial.ac.uk (S. Naderi), z.sharif-khodaei@imperial.ac.uk (Z. Sharif Khodaei), m.h.aliabadi@imperial.ac.uk (F.M.H. 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.2026.02.008