https://www.scipedia.com/wd/index.php?action=history&feed=atom&title=Reyes_et_al_2018bReyes et al 2018b - Revision history2026-06-11T22:53:19ZRevision history for this page on the wikiMediaWiki 1.27.0-wmf.10https://www.scipedia.com/wd/index.php?title=Reyes_et_al_2018b&diff=202725&oldid=prevScipediacontent: Scipediacontent moved page Draft Content 324645808 to Reyes et al 2018b2021-02-02T06:28:44Z<p>Scipediacontent moved page <a href="/public/Draft_Content_324645808" class="mw-redirect" title="Draft Content 324645808">Draft Content 324645808</a> to <a href="/public/Reyes_et_al_2018b" title="Reyes et al 2018b">Reyes et al 2018b</a></p>
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</td></tr></table>Scipediacontenthttps://www.scipedia.com/wd/index.php?title=Reyes_et_al_2018b&diff=202724&oldid=prevScipediacontent: Created page with " == Abstract == This article presents a methodology to automate the prediction of maintenance intervention alerts in transport linear infrastructures. It combines current and..."2021-02-02T06:28:40Z<p>Created page with " == Abstract == This article presents a methodology to automate the prediction of maintenance intervention alerts in transport linear infrastructures. It combines current and..."</p>
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== Abstract ==<br />
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This article presents a methodology to automate the prediction of maintenance intervention alerts in transport linear infrastructures. It combines current and predicted asset conditions with operational and historical maintenance data, to predict the needed tasks to avoid later severe degradation. By means of data analytics and machine learning models, a prioritised listing ranked on severity level corresponding to the alerts generated for all assets of the infrastructure is inferred. The scientific part presents: a discussion on relevant data to train machine learning algorithms in order to generate reliable predictions of the interventions to be carried out in further time scenarios, a schematic flow chart of the automatic learning procedure, and the self-learning rules from automatic learning from false positive/negatives. The empirical part describes a road network pilot case, the available historical data information, measurements, maintenance interventions, and a selected set of outcomes.<br />
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== Original document ==<br />
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The different versions of the original document can be found in:<br />
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* [https://zenodo.org/record/1445993 https://zenodo.org/record/1445993] under the license http://creativecommons.org/licenses/by-nc-nd/4.0/legalcode<br />
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* [https://zenodo.org/record/1445993 https://zenodo.org/record/1445993],<br />
: [http://dx.doi.org/10.5281/zenodo.1445992 http://dx.doi.org/10.5281/zenodo.1445992] under the license http://creativecommons.org/licenses/by-nc-nd/4.0/legalcode<br />
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* [https://zenodo.org/record/1445993 https://zenodo.org/record/1445993],<br />
: [http://dx.doi.org/10.5281/zenodo.1445993 http://dx.doi.org/10.5281/zenodo.1445993] under the license http://creativecommons.org/licenses/by-nc-nd/4.0/legalcode<br />
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DOIS: 10.5281/zenodo.1445993 10.5281/zenodo.1445992</div>Scipediacontent