Constrained Surrogate-based Optimisation for Finite Element Model Updating
Eduardo LARREA HERRANZ, Gabriele DESSENA
Abstract. Finite element model updating represents a pivotal process in civil aircraft development, enabling the reconciliation of numerical models with experimental data obtained from static or ground vibration testing. Traditional surrogate-based optimisation methods, particularly Efficient Global Optimization through Kriging modelling, often lack robust constraint handling mechanisms, which are essential for real-world aerospace applications. This study presents the development and validation of a Constrained refined Efficient Global Optimisation (CrEGO) framework that addresses this limitation by integrating a Probability of Feasibility-penalised Expected Improvement acquisition function into the refined EGO methodology. The CrEGO algorithm constructs Kriging surrogate models for both objective and constraint functions, enabling rapid feasibility assessment without costly evaluations. Validation on the eXperimental BearDS 2 high-aspect-ratio wing spar demonstrates remarkable performance improvements, reducing the Modified Total Modal Assurance Criterion from 0.214 to 0.006 (97% improvement) whilst maintaining strict mass constraints, using only 829 expensive finite element evaluations.
Keywords
Surrogate-Based Optimisation, Finite Element Model Updating, Constrained Optimisation, Kriging, Modal Analysis
Published online 7/20/2026, 5 pages
Copyright © 2026 by the author(s)
Published under license by Materials Research Forum LLC., Millersville PA, USA
Citation: Eduardo LARREA HERRANZ, Gabriele DESSENA, Constrained Surrogate-based Optimisation for Finite Element Model Updating, Materials Research Proceedings, Vol. 69, pp 1336-1340, 2026
DOI: https://doi.org/10.21741/9781644904251-232
The article was published as article 232 of the book CEAS – AIDAA Conference 2025
Content from this work may be used under the terms of the Creative Commons Attribution 3.0 license. Any further distribution of this work must maintain attribution to the author(s) and the title of the work, journal citation and DOI.
References
[1] Marwala T, Sibisi S. Finite Element Model Updating Using Bayesian Framework and Modal Properties. J Aircr 2005;42:275–8. https://doi.org/10.2514/1.11841.
[2] Rosati I, Fabbrocino G, Rainieri C. A discussion about the Douglas-Reid model updating method and its prospective application to continuous vibration-based SHM of a historical building. Eng Struct 2022;273:115058. https://doi.org/10.1016/j.engstruct.2022.115058.
[3] Raviolo D, Civera M, Zanotti Fragonara L. A Comparative Analysis of Optimization Algorithms for Finite Element Model Updating on Numerical and Experimental Benchmarks. Buildings 2023;13:3010. https://doi.org/10.3390/buildings13123010.
[4] Jones DR, Schonlau M, Welch WJ. Efficient Global Optimization of Expensive Black-Box Functions. J Glob Optim 1998;13:455–92. https://doi.org/10.1023/A:1008306431147.
[5] Dessena G, Ignatyev DI, Whidborne JF, Zanotti Fragonara L. A global–local meta-modelling technique for model updating. Comput Methods Appl Mech Eng 2024;418:116511. https://doi.org/10.1016/j.cma.2023.116511.
[6] Forrester AIJ, Sóbester A, Keane AJ. Engineering Design via Surrogate Modelling. Wiley; 2008. https://doi.org/10.1002/9780470770801.
[7] Bagheri S, Konen W, Allmendinger R, Branke J, Deb K, Fieldsend J, et al. Constraint handling in efficient global optimization. Proc. Genet. Evol. Comput. Conf., New York, NY, USA: ACM; 2017, p. 673–80. https://doi.org/10.1145/3071178.3071278.
[8] Yusuf SY, Hayes D, Pontillo A, Carrizales MA, Dussart GX, Lone MM. Aeroelastic Scaling for Flexible High Aspect Ratio Wings. AIAA Scitech 2019 Forum, Reston, Virginia: American Institute of Aeronautics and Astronautics; 2019, p. 1–14. https://doi.org/10.2514/6.2019-1594.
[9] Dessena G, Ignatyev DI, Whidborne JF, Pontillo A, Zanotti Fragonara L. Ground vibration testing of a flexible wing: A benchmark and case study. Aerospace 2022;9:438. https://doi.org/10.3390/aerospace9080438.
[10] Perera R, Torres R. Structural Damage Detection via Modal Data with Genetic Algorithms. J Struct Eng 2006;132:1491–501. https://doi.org/10.1061/(ASCE)0733-9445(2006)132:9(1491).
