20.500.12556/RUL-174160 A novel approach to surrogate modelling of modal properties mode-shape-adapted input parameter domain cutting Surrogate models, also known as meta-models or proxy models, have become invaluable in structural engineering. They are a great addition to the finite element models, providing a fast computational alternative for approximating the quantity of interest (QOI). By the quick evaluation of the surrogate model, they can accelerate stochastic analyses of the structural response under the uncertainties of its input parameters (such as uncertainty quantification and sensitivity analysis) as well as the processes of optimisation and probabilistic model updating. They also offer an offline computation of the QOI which is particularly beneficial in scenarios of structural health monitoring where access to licenced software is limited. Surrogate modelling of modal properties is particularly challenging due to the mode degeneration phenomena, such as mode crossing, veering, and coalescence. The paper introduces a novel approach to surrogate modelling of modal properties that is accurate and reduces the required number of training points. The here-introduced mode-shape-adapted input parameter domain cutting (MOSAIC) surrogate modelling technique is a form of piecewise approximation. The novelty of this approach lies in the intelligent cutting of the parameter domain into subdomains, which identifies regions where the mode shapes smoothly change. As with all black-box surrogate modelling techniques, the method requires only a set of parameter samples and the computation of the corresponding QOIs (here the modal properties) by the finite element model. The paper presents the method in detail and provides three examples with two, six, and seven input parameters, respectively. In all of the examples, mode degeneration phenomena are present. The MOSIAC surrogate model achieves significantly better accuracy than the benchmark surrogate model, which is trained over the whole parameter domain without cutting it. The accuracy of the MOSAIC surrogate model outperforms even the benchmark model that is trained on ten times as many training points. This indicates a large time-saving potential in building surrogate models of modal properties. The accuracy and efficiency of the MOSAIC method are further enhanced by the proposed active learning approach. surrogate model proxy model metamodel modal properties natural frequency mode shape eigenvector Mosaic nadomestni model modalne lastnosti lastna frekvenca lastna oblika lastni vektor true false false Angleški jezik Slovenski jezik Članek v reviji 2025-09-29 07:39:31 2025-09-29 07:39:41 2025-11-19 03:31:09 0000-00-00 00:00:00 2025 0 0 20 str. Vol. 240, art. 113381 Nov. 2025 0000-00-00 Zaloznikova Objavljeno NiDoloceno 0000-00-00 0000-00-00 0000-00-00 519.62:510.643 1096-1216 10.1016/j.ymssp.2025.113381 250774787 Kurent...-A_novel_approach_to_surrogate_modelling_of_modal_properties.pdf Kurent...-A_novel_approach_to_surrogate_modelling_of_modal_properties.pdf 1 E16D7BBE300EBFC0F3874FD744D41F9F ff9542f3f09bc4e2cce1b0cd901a59c0919d5804a50100a96c15990f227989ca c8ad19d6-9cf6-11f0-9328-0050569b8976 https://repozitorij.uni-lj.si/Dokument.php?lang=slv&id=218921 https://www.sciencedirect.com/science/article/pii/S0888327025010829 1 819ba964-9cf6-11f0-9328-0050569b8976 https://repozitorij.uni-lj.si/Dokument.php?lang=slv&id=218920 Fakulteta za gradbeništvo in geodezijo 0 0 0