The DOI matches its Crossref record; 299 of the 302 references it cites were found in a registry or an index, and 3 are not checked yet.
Review of finite element model updating methods for structural applications
Open accessAbstract
At the time of designing structures up to date, the density and magnitude of the load have increased, and the requirements for regulation have also become more stringent. To ensure the essential requirements, especially the mechanical resistance and stability, the numerical modelling of the structure is carried out according to the current regulations. Due to various assumptions, idealization, discretization, and parameterizations that are introduced numerical modelling, obtained numerical model may not always reflect the actual structural behavior. It is known that these structures have a hidden resistance that can be determined by combining experimental investigations (static or/and dynamic tests) and finite element model updating methods to minimize the differences between the actual and predicted structural behavior. This paper provides a review of the FEMU process and methods used and summarizes the FEMU approach to help future engineers to select the appropriate method for solving some discussed issues. First, the main terms important for understanding FEMU are introduced. The whole process of model updating is described step by step: selection of updating parameters (design variables), definition of the model updating problem, its solution using different FEMU methods. An overview of the following methods is given: sensitivity-based, maximum likelihood, non-probabilistic, probabilistic, response surface and regularization methods. Each of the method is presented with the corresponding mathematical background, implementation steps, and examples of studies from the literature.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
The source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“Simplifying assumptions for some analytical techniques may lead to inaccuracies when dealing with complex geometry, such as sandwich structures with nonlinear materials or intricate failure modes [26].”
From Machine Learning Algorithms for Prediction and Characterization of Cohesive Zone Parameters for Mixed-Mode Fracture (2024) · cites this paper as [26]
Experimental — accuracy not yet measured
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Zadeh, L. A. (1965) · Information and Control · doi:10.1016/s0019-9958(65)90241-x · Cited by 67,855
Optimization by Simulated Annealing
Kirkpatrick, S., Gelatt, C. D., & Vecchi, M. P. (1983) · Science · doi:10.1126/science.220.4598.671 · Cited by 45,134
Equation of State Calculations by Fast Computing Machines
Metropolis, N., Rosenbluth, A. W., Rosenbluth, M. N., Teller, A. H., & Teller, E. (1953) · The Journal of Chemical Physics · doi:10.1063/1.1699114 · Cited by 37,192
Bathe, K. (1995) · CERN Document Server (European Organization for Nuclear Research) · Cited by 10,651
GSA: A Gravitational Search Algorithm
Rashedi, E., Nezamabadi–pour, H., & Saryazdi, S. (2009) · Information Sciences · doi:10.1016/j.ins.2009.03.004 · Cited by 7,501
Regression Shrinkage and Selection via The Lasso: A Retrospective
Tibshirani, R. J. (2011) · Journal of the Royal Statistical Society Series B (Statistical Methodology) · doi:10.1111/j.1467-9868.2011.00771.x · Cited by 3,601
Modal Testing: Theory, Practice, and Application
Ewins, D. J. (1998) · Cited by 2,667
Recent advances in surrogate-based optimization
Forrester, A. I. J., & Keane, A. J. (2009) · Progress in Aerospace Sciences · doi:10.1016/j.paerosci.2008.11.001 · Cited by 2,475
Surrogate-based analysis and optimization
Queipo, N. V., Haftka, R. T., Shyy, W., Goel, T., Vaidyanathan, R., & Tucker, P. K. (2005) · Progress in Aerospace Sciences · doi:10.1016/j.paerosci.2005.02.001 · Cited by 2,465
Finite Element Model Updating in Structural Dynamics
Friswell, M. I., & Mottershead, J. E. (1995) · Solid Mechanics and its Applications · doi:10.1007/978-94-015-8508-8 · Cited by 2,452
Research works printed but not linked: 16
- Altunişik, 2018, Automated Model Updating of Historical Masonry Structures Based on Ambient Vibration Measurements, J Perform Constr Facil, 32, 04017126, 10.1061/(ASCE)CF.1943-5509.0001108 · DOI
- Sen S.,Bhattacharya B. Eigen structure assignment based finite element model updating, in: Int. Conf. Comput. Aided Eng., Chennai, 2013.
- Wang, 2016, 3rd Int, Conf Inf Sci Control Eng ICISCE, 2016, 640
- Eltouny K.,Liang X. A nonparametric unsupervised learning approach for structural damage detection. ArXiv 2020.
- R. Moore R.B. Kearfott M.J. Cloud Interval, Analysis. Society for industrial and Applied Mathematics 2009 Philadelphia 10.1007/1-84628-108-3_18. · DOI
- Mohan, 2010, Discussion on Mathematical Modeling of Fuzzy Two-Term (PI / PD), Controllers, 8, 38
- Kaveh, 2016, Damage identification of truss structures using CBO and ECBO algorithms, Asian J Civ Eng, 17, 75
- Zong Woo Geem, 2001, A new heuristic optimization algorithm: Harmony search. A New Heuristic Optim. Algorithm Harmon, Search, 76, 60
- Wu, 2018, Finite Element Model Updating in Bridge Structures Using Kriging Model and Latin Hypercube Sampling Method, Adv Civ Eng, 2018, 1
- Conn, 2019, Introduction to Derivative-Free Optimization. MPS-SIAM Series on, Optimization
- A. Jung Machine Learning: Fundations, Methodologies and Applications 1st editio, 2018 Springer Singapore 10.1007/978-981-16-8193-6. · DOI
- Zhu Y.,Zhang L. Finite element model updating based on least squares support vector machines. 2009. https://doi.org/10.1007/978-3-642-01510-6_34. · DOI
- Ivanova, 2016, We are IntechOpen, the world ’ s leading publisher of Open Access books Built by scientists, for scientists TOP 1 %, Intech, i, 13
- Ben, 2018, Model selection and adaptive sampling in surrogate modeling, Kriging and beyond
- Grabec, 1990, Biological Cybemetics, Neural Networks, 409, 403
- Кapycкeвич M.,Кopчyк O.,Лicoвcькa M. Structural Health monitoring, in: A. Wicks (Ed.), A Conf. Expo. Struct. Dyn., Springer, 2013: pp. 117–122.
Cited by
Sorted by citations
Review on smartphone sensing technology for structural health monitoring
Sarmadi, H., Entezami, A., Yuen, K., & Behkamal, B. (2023) · Measurement · doi:10.1016/j.measurement.2023.113716 · Cited by 113
Advances in artificial intelligence for structural health monitoring: A comprehensive review
Spencer, B. F., Sim, S., Kim, R. E., & Yoon, H. (2025) · KSCE Journal of Civil Engineering · doi:10.1016/j.kscej.2025.100203 · Cited by 98
Finite Element Model Updating for Material Model Calibration: A Review and Guide to Practice
Chen, B., Starman, B., Halilovič, M., Berglund, L. A., & Coppieters, S. (2024) · Archives of Computational Methods in Engineering · doi:10.1007/s11831-024-10200-9 · Cited by 85
Damage identification of large-scale space truss structures based on stiffness separation method
Xiao, F., Sun, H., Mao, Y., & Chen, G. S. (2023) · Structures · doi:10.1016/j.istruc.2023.04.027 · Cited by 53
Kamali, S., Mariani, S., Hadianfard, M. A., & Marzani, A. (2024) · Mechanical Systems and Signal Processing · doi:10.1016/j.ymssp.2024.111416 · Cited by 52
Metadata
- Authors
- Ereiz, S., Duvnjak, I., & Fernando Jiménez-Alonso, J.
- Affiliations
- University of Zagreb (HR)
- Corresponding author
- Ereiz, Suzana
- Venue
- Structures
- Publisher
- Elsevier BV
- Published
- 2022-07
- Volume
- 41
- Pages
- 684-723
- ISSN
- 2352-0124 (ISSN-L)
- OpenAlex
- W4280574223
- Cited by
- 371
- References
- 292
- Citations by year
- 2026: 94 · 2025: 138 · 2024: 90 · 2023: 44 · 2022: 5
- FWCI
- 31.13
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Structures (publishedVersion, cc-by)
- idUS (Universidad de Sevilla) (submittedVersion, cc-by)
- Funders
- University of Zagreb
- European Commission
- European Regional Development Fund (KK.01.1.1.04.0041)
- Topics
- Physical Sciences › Engineering › Civil and Structural Engineering › Structural Health Monitoring Techniques
- Physical Sciences › Engineering › Control and Systems Engineering › Control Systems and Identification
- Physical Sciences › Mathematics › Applied Mathematics › Statistical and numerical algorithms
- Keywords
- finite element model updating, structural identification, design variables, response surface methodology, regularization methods, structural behavior
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
