{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,8]],"date-time":"2026-04-08T07:30:59Z","timestamp":1775633459655,"version":"3.50.1"},"reference-count":39,"publisher":"MDPI AG","issue":"10","license":[{"start":{"date-parts":[[2022,5,16]],"date-time":"2022-05-16T00:00:00Z","timestamp":1652659200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"2019 Research and Innovation Programme of Ente Cassa di Risparmio di Firenze"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Roads are a strategic asset of a country and are of great importance for the movement of passengers and goods. Increasing traffic volume and load, together with the aging of roads, creates various types of anomalies on the road surface. This work proposes a low-cost system for real-time screening of road pavement conditions. Acceleration signals provided by on-car sensors are processed in the time\u2013frequency domain in order to extract information about the condition of the road surface. More specifically, a short-time Fourier transform is used, and significant features, such as the coefficient of variation and the entropy computed over the energy of segments of the signal, are exploited to distinguish between well-localized pavement distresses caused by potholes and manhole covers and spread distress due to fatigue cracking and rutting. The extracted features are fed to supervised machine learning classifiers in order to distinguish the pavement distresses. System performance is assessed using real data, collected by sensors located on the car\u2019s dashboard and floorboard and manually labeled. The experimental results show that the proposed system is effective at detecting the presence and the type of distress with high classification rates.<\/jats:p>","DOI":"10.3390\/s22103788","type":"journal-article","created":{"date-parts":[[2022,5,16]],"date-time":"2022-05-16T21:36:06Z","timestamp":1652736966000},"page":"3788","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":38,"title":["Road Surface Anomaly Assessment Using Low-Cost Accelerometers: A Machine Learning Approach"],"prefix":"10.3390","volume":"22","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8509-5322","authenticated-orcid":false,"given":"Alessio","family":"Martinelli","sequence":"first","affiliation":[{"name":"Telespazio, Via Tiburtina 965, 00156 Rome, Italy"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0878-7381","authenticated-orcid":false,"given":"Monica","family":"Meocci","sequence":"additional","affiliation":[{"name":"Department of Civil and Environmental Engineering, University of Florence, 50139 Florence, Italy"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5520-7322","authenticated-orcid":false,"given":"Marco","family":"Dolfi","sequence":"additional","affiliation":[{"name":"Department of Information Engineering, University of Florence, 50139 Florence, Italy"}]},{"given":"Valentina","family":"Branzi","sequence":"additional","affiliation":[{"name":"Department of Civil and Environmental Engineering, University of Florence, 50139 Florence, Italy"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0145-8406","authenticated-orcid":false,"given":"Simone","family":"Morosi","sequence":"additional","affiliation":[{"name":"Department of Information Engineering, University of Florence, 50139 Florence, Italy"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7776-4015","authenticated-orcid":false,"given":"Fabrizio","family":"Argenti","sequence":"additional","affiliation":[{"name":"Department of Information Engineering, University of Florence, 50139 Florence, Italy"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9897-1387","authenticated-orcid":false,"given":"Lorenzo","family":"Berzi","sequence":"additional","affiliation":[{"name":"Department of Industrial Engineering, University of Florence, 50139 Florence, Italy"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5738-9991","authenticated-orcid":false,"given":"Tommaso","family":"Consumi","sequence":"additional","affiliation":[{"name":"Department of Information Engineering, University of Florence, 50139 Florence, Italy"}]}],"member":"1968","published-online":{"date-parts":[[2022,5,16]]},"reference":[{"key":"ref_1","unstructured":"Queiroz, C., and Gautam, S. 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