{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,9]],"date-time":"2026-08-09T09:18:54Z","timestamp":1786267134289,"version":"3.56.0"},"reference-count":41,"publisher":"MDPI AG","issue":"5","license":[{"start":{"date-parts":[[2021,2,27]],"date-time":"2021-02-27T00:00:00Z","timestamp":1614384000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Current maintenance intervals of mechanical systems are scheduled a priori based on the life of the system, resulting in expensive maintenance scheduling, and often undermining the safety of passengers. Going forward, the actual usage of a vehicle will be used to predict stresses in its structure, and therefore, to define a specific maintenance scheduling. Machine learning (ML) algorithms can be used to map a reduced set of data coming from real-time measurements of a structure into a detailed\/high-fidelity finite element analysis (FEA) model of the same system. As a result, the FEA-based ML approach will directly estimate the stress distribution over the entire system during operations, thus improving the ability to define ad-hoc, safe, and efficient maintenance procedures. The paper initially presents a review of the current state-of-the-art of ML methods applied to finite elements. A surrogate finite element approach based on ML algorithms is also proposed to estimate the time-varying response of a one-dimensional beam. Several ML regression models, such as decision trees and artificial neural networks, have been developed, and their performance is compared for direct estimation of the stress distribution over a beam structure. The surrogate finite element models based on ML algorithms are able to estimate the response of the beam accurately, with artificial neural networks providing more accurate results.<\/jats:p>","DOI":"10.3390\/s21051654","type":"journal-article","created":{"date-parts":[[2021,2,27]],"date-time":"2021-02-27T20:55:44Z","timestamp":1614459344000},"page":"1654","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":85,"title":["A Machine Learning Approach as a Surrogate for a Finite Element Analysis: Status of Research and Application to One Dimensional Systems"],"prefix":"10.3390","volume":"21","author":[{"given":"Poojitha","family":"Vurtur Badarinath","sequence":"first","affiliation":[{"name":"Computer Engineering Department, San Jose\u2019 State University, San Jose, CA 95192, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9093-8697","authenticated-orcid":false,"given":"Maria","family":"Chierichetti","sequence":"additional","affiliation":[{"name":"Department of Aerospace Engineering, San Jose\u2019 State University, San Jose, CA 95192, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8088-7948","authenticated-orcid":false,"given":"Fatemeh","family":"Davoudi Kakhki","sequence":"additional","affiliation":[{"name":"Machine Learning and Safety Analytics Lab, Department of Technology, San Jose\u2019 State University, San Jose, CA 95192, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2021,2,27]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Smarsly, K., Lehner, K., and Hartmann, D. 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