{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,19]],"date-time":"2026-01-19T00:02:47Z","timestamp":1768780967609,"version":"3.49.0"},"reference-count":60,"publisher":"MDPI AG","issue":"8","license":[{"start":{"date-parts":[[2024,8,22]],"date-time":"2024-08-22T00:00:00Z","timestamp":1724284800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Base Funding","award":["UIDB\/04708\/2020"],"award-info":[{"award-number":["UIDB\/04708\/2020"]}]},{"name":"Base Funding","award":["UIDP\/04708\/2020"],"award-info":[{"award-number":["UIDP\/04708\/2020"]}]},{"name":"Base Funding","award":["2021.04272.CEECIND"],"award-info":[{"award-number":["2021.04272.CEECIND"]}]},{"name":"Programmatic Funding","award":["UIDB\/04708\/2020"],"award-info":[{"award-number":["UIDB\/04708\/2020"]}]},{"name":"Programmatic Funding","award":["UIDP\/04708\/2020"],"award-info":[{"award-number":["UIDP\/04708\/2020"]}]},{"name":"Programmatic Funding","award":["2021.04272.CEECIND"],"award-info":[{"award-number":["2021.04272.CEECIND"]}]},{"name":"national funds through the FCT\/ MCTES (PIDDAC)","award":["UIDB\/04708\/2020"],"award-info":[{"award-number":["UIDB\/04708\/2020"]}]},{"name":"national funds through the FCT\/ MCTES (PIDDAC)","award":["UIDP\/04708\/2020"],"award-info":[{"award-number":["UIDP\/04708\/2020"]}]},{"name":"national funds through the FCT\/ MCTES (PIDDAC)","award":["2021.04272.CEECIND"],"award-info":[{"award-number":["2021.04272.CEECIND"]}]},{"name":"Stimulus of Scientific Employment, Individual Support (CEECIND)\u20144rd Edition provided by \u201cFCT\u2014Funda\u00e7\u00e3o para a Ci\u00eancia\u201d","award":["UIDB\/04708\/2020"],"award-info":[{"award-number":["UIDB\/04708\/2020"]}]},{"name":"Stimulus of Scientific Employment, Individual Support (CEECIND)\u20144rd Edition provided by \u201cFCT\u2014Funda\u00e7\u00e3o para a Ci\u00eancia\u201d","award":["UIDP\/04708\/2020"],"award-info":[{"award-number":["UIDP\/04708\/2020"]}]},{"name":"Stimulus of Scientific Employment, Individual Support (CEECIND)\u20144rd Edition provided by \u201cFCT\u2014Funda\u00e7\u00e3o para a Ci\u00eancia\u201d","award":["2021.04272.CEECIND"],"award-info":[{"award-number":["2021.04272.CEECIND"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Algorithms"],"abstract":"<jats:p>One of the primary challenges in the railway industry revolves around achieving a comprehensive and insightful understanding of track conditions. The geometric parameters and stiffness of railway tracks play a crucial role in condition monitoring as well as maintenance work. Hence, this study investigated the relationship between vertical ballast stiffness and the track longitudinal level. Initially, the ballast stiffness and track longitudinal level data were acquired through a series of experimental measurements conducted on a reference test track along the Tehran\u2013Mashhad railway line, utilizing recording cars for geometric track and stiffness recordings. Subsequently, the correlation between the track longitudinal level and ballast stiffness was surveyed using both frequency-based techniques and machine learning (ML) algorithms. The power spectrum density (PSD) as a frequency-based technique was employed, alongside ML algorithms, including linear regression, decision trees, and random forests, for correlation mining analyses. The results showed a robust and statistically significant relationship between the vertical ballast stiffness and longitudinal levels of railway tracks. Specifically, the PSD data exhibited a considerable correlation, especially within the 1\u20134 rad\/m wave number range. Furthermore, the data analyses conducted using ML methods indicated that the values of the root mean square error (RMSE) were about 0.05, 0.07, and 0.06 for the linear regression, decision tree, and random forest algorithms, respectively, demonstrating the adequate accuracy of ML-based approaches.<\/jats:p>","DOI":"10.3390\/a17080372","type":"journal-article","created":{"date-parts":[[2024,8,22]],"date-time":"2024-08-22T06:28:51Z","timestamp":1724308131000},"page":"372","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["Correlation Analysis of Railway Track Alignment and Ballast Stiffness: Comparing Frequency-Based and Machine Learning Algorithms"],"prefix":"10.3390","volume":"17","author":[{"given":"Saeed","family":"Mohammadzadeh","sequence":"first","affiliation":[{"name":"School of Railway Engineering, Iran University of Science and Technology, Tehran 16846, Iran"}]},{"given":"Hamidreza","family":"Heydari","sequence":"additional","affiliation":[{"name":"School of Railway Engineering, Iran University of Science and Technology, Tehran 16846, Iran"}]},{"given":"Mahdi","family":"Karimi","sequence":"additional","affiliation":[{"name":"School of Railway Engineering, Iran University of Science and Technology, Tehran 16846, Iran"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6751-0388","authenticated-orcid":false,"given":"Araliya","family":"Mosleh","sequence":"additional","affiliation":[{"name":"Faculty of Engineering, University of Porto, 4200-465 Porto, Portugal"}]}],"member":"1968","published-online":{"date-parts":[[2024,8,22]]},"reference":[{"key":"ref_1","unstructured":"Esveld, C. 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