{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,6]],"date-time":"2026-03-06T16:07:30Z","timestamp":1772813250111,"version":"3.50.1"},"reference-count":0,"publisher":"IOS Press","isbn-type":[{"value":"9781643686547","type":"electronic"}],"license":[{"start":{"date-parts":[[2026,3,4]],"date-time":"2026-03-04T00:00:00Z","timestamp":1772582400000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2026,3,4]]},"abstract":"<jats:p>To avoid the difficulties of selecting frequency points in model updating methods using frequency response functions, reduce the output response dimensions, and enhance updating efficiency, a new approach combining wavelet packet decomposition and Kriging model is proposed. Initially, the calculated frequency response function is subjected to wavelet packet decomposition, extracting the energy of the last layer after decomposition to represent the structural frequency response. Subsequently, Latin hypercube sampling is employed to design the initially selected parameters for updating, followed by sensitivity analysis to identify which parameters should be updated. Then, these selected parameters are fed into the Kriging model as inputs, while the energy extracted from the final decomposition layer serves as the model output, resulting in an accurate and efficient Kriging model. Finally, the goal is to minimize the difference between the energy of the last layer extracted from the target frequency response function after wavelet packet decomposition and the energy of the last layer output by the Kriging model. Simulation examples show that using the energy of the last layer extracted after wavelet packet decomposition as the structural response achieves higher updating precision. The updated finite element model almost coincides with the actual structure\u2019s frequency response function and can replace the actual structure for dynamic analysis.<\/jats:p>","DOI":"10.3233\/faia260016","type":"book-chapter","created":{"date-parts":[[2026,3,6]],"date-time":"2026-03-06T10:20:49Z","timestamp":1772792449000},"source":"Crossref","is-referenced-by-count":0,"title":["Finite Element Model Updating Method Based on Wavelet Packet Decomposition and Kriging Model"],"prefix":"10.3233","author":[{"ORCID":"https:\/\/orcid.org\/0009-0003-8130-6393","authenticated-orcid":false,"given":"Lei","family":"Wang","sequence":"first","affiliation":[{"name":"Nanjing Vocational Institute of Railway Technology, Nanjing, Jiangsu 210031, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"7437","container-title":["Frontiers in Artificial Intelligence and Applications","Machine Learning and Artificial Intelligence"],"original-title":[],"link":[{"URL":"https:\/\/ebooks.iospress.nl\/pdf\/doi\/10.3233\/FAIA260016","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,3,6]],"date-time":"2026-03-06T10:20:49Z","timestamp":1772792449000},"score":1,"resource":{"primary":{"URL":"https:\/\/ebooks.iospress.nl\/doi\/10.3233\/FAIA260016"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,3,4]]},"ISBN":["9781643686547"],"references-count":0,"URL":"https:\/\/doi.org\/10.3233\/faia260016","relation":{},"ISSN":["0922-6389","1879-8314"],"issn-type":[{"value":"0922-6389","type":"print"},{"value":"1879-8314","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,3,4]]}}}