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For this reason, predictive mechanisms are needed to detect and address the corrosion phenomenon. Recently, we have seen a first attempt at leveraging Machine Learning (ML) techniques in this field thanks to their high ability in modeling highly complex phenomena. In order to rely on these techniques, we need a set of data, representing factors influencing the corrosion in a given pipeline, together with their related supervised information, measuring the corrosion level along the considered infrastructure profile. Unfortunately, it is not always possible to access supervised information for a given pipeline since measuring the corrosion is a costly and time-consuming operation. In this paper, we will address the problem of devising a ML-based predictive model for internal corrosion under the assumption that supervised information is unavailable for the pipeline of interest, while it is available for some other pipelines that can be leveraged through Transfer Learning (TL) to build the predictive model itself. We will cover all the methodological steps from data set creation to the usage of TL. The whole methodology will be experimentally validated on a set of real-world pipelines.<\/jats:p>","DOI":"10.1007\/s10489-021-02771-y","type":"journal-article","created":{"date-parts":[[2021,10,6]],"date-time":"2021-10-06T10:12:32Z","timestamp":1633515152000},"page":"7622-7637","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":19,"title":["A transfer-learning approach for corrosion prediction in pipeline infrastructures"],"prefix":"10.1007","volume":"52","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8647-6269","authenticated-orcid":false,"given":"Giuseppe","family":"Canonaco","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Manuel","family":"Roveri","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Cesare","family":"Alippi","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Fabrizio","family":"Podenzani","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Antonio","family":"Bennardo","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Marco","family":"Conti","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Nicola","family":"Mancini","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2021,10,6]]},"reference":[{"issue":"7579","key":"2771_CR1","doi-asserted-by":"publisher","first-page":"441","DOI":"10.1038\/527441a","volume":"527","author":"X Li","year":"2015","unstructured":"Li X, Zhang D, Liu Z, Li Z, Du C, Dong C (2015) Materials science: Share corrosion data. 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