{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,26]],"date-time":"2026-06-26T09:54:49Z","timestamp":1782467689694,"version":"3.54.5"},"reference-count":61,"publisher":"MDPI AG","issue":"17","license":[{"start":{"date-parts":[[2023,8,31]],"date-time":"2023-08-31T00:00:00Z","timestamp":1693440000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001602","name":"Science Foundation Ireland","doi-asserted-by":"publisher","award":["20\/FFP-P\/8706"],"award-info":[{"award-number":["20\/FFP-P\/8706"]}],"id":[{"id":"10.13039\/501100001602","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>In this paper, a novel railway track monitoring approach is proposed that employs acceleration responses measured on an in-service train to detect the loss of stiffness in the track sub-layers. An Artificial Neural Network (ANN) algorithm is developed that works with the energies of the train acceleration responses. A numerical model of a half-car train coupled with a track profile is employed to simulate the train vertical acceleration. The energy of acceleration signals measured from 100 traversing trains is used to train the ANN for healthy track conditions. The energy is calculated every 15 m along the track, each of which is called a slice. In the monitoring phase, the trained ANN is used to predict the energies of a set of train crossings. The predicted energies are compared with the simulated ones and represented as the prediction error. The damage is modeled by reducing the soil stiffness at the sub-ballast layer that represents hanging sleepers. A damage indicator (DI) based on the prediction error is proposed to visualize the differences in the predicted energies for different damage cases. In addition, a sensitivity analysis is performed where the impact of signal noise, slice sizes, and the presence of multiple damaged locations on the performance of the DI is assessed.<\/jats:p>","DOI":"10.3390\/s23177568","type":"journal-article","created":{"date-parts":[[2023,8,31]],"date-time":"2023-08-31T11:45:51Z","timestamp":1693482351000},"page":"7568","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":31,"title":["A Machine-Learning-Based Approach for Railway Track Monitoring Using Acceleration Measured on an In-Service Train"],"prefix":"10.3390","volume":"23","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1358-1943","authenticated-orcid":false,"given":"Abdollah","family":"Malekjafarian","sequence":"first","affiliation":[{"name":"Structural Dynamics and Assessment Laboratory, School of Civil Engineering, University College Dublin, D04V1W8 Dublin, Ireland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chalres-Antoine","family":"Sarrabezolles","sequence":"additional","affiliation":[{"name":"The \u00c9cole Nationale des Travaux Publics de l\u2019\u00c9tat (ENTPE), 69518 Lyon, France"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2930-7685","authenticated-orcid":false,"given":"Muhammad Arslan","family":"Khan","sequence":"additional","affiliation":[{"name":"Structural Dynamics and Assessment Laboratory, School of Civil Engineering, University College Dublin, D04V1W8 Dublin, Ireland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Fatemeh","family":"Golpayegani","sequence":"additional","affiliation":[{"name":"School of Computer Science, University College Dublin, D04V1W8 Dublin, Ireland"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2023,8,31]]},"reference":[{"key":"ref_1","unstructured":"Schneider, A. 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