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Road Surface Anomaly Assessment Using Low-Cost Accelerometers: A Machine Learning Approach

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...

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Autores principales: Martinelli, Alessio, Meocci, Monica, Dolfi, Marco, Branzi, Valentina, Morosi, Simone, Argenti, Fabrizio, Berzi, Lorenzo, Consumi, Tommaso
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9146565/
https://www.ncbi.nlm.nih.gov/pubmed/35632196
http://dx.doi.org/10.3390/s22103788
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author Martinelli, Alessio
Meocci, Monica
Dolfi, Marco
Branzi, Valentina
Morosi, Simone
Argenti, Fabrizio
Berzi, Lorenzo
Consumi, Tommaso
author_facet Martinelli, Alessio
Meocci, Monica
Dolfi, Marco
Branzi, Valentina
Morosi, Simone
Argenti, Fabrizio
Berzi, Lorenzo
Consumi, Tommaso
author_sort Martinelli, Alessio
collection PubMed
description 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–frequency 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’s 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.
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spelling pubmed-91465652022-05-29 Road Surface Anomaly Assessment Using Low-Cost Accelerometers: A Machine Learning Approach Martinelli, Alessio Meocci, Monica Dolfi, Marco Branzi, Valentina Morosi, Simone Argenti, Fabrizio Berzi, Lorenzo Consumi, Tommaso Sensors (Basel) Article 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–frequency 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’s 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. MDPI 2022-05-16 /pmc/articles/PMC9146565/ /pubmed/35632196 http://dx.doi.org/10.3390/s22103788 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Martinelli, Alessio
Meocci, Monica
Dolfi, Marco
Branzi, Valentina
Morosi, Simone
Argenti, Fabrizio
Berzi, Lorenzo
Consumi, Tommaso
Road Surface Anomaly Assessment Using Low-Cost Accelerometers: A Machine Learning Approach
title Road Surface Anomaly Assessment Using Low-Cost Accelerometers: A Machine Learning Approach
title_full Road Surface Anomaly Assessment Using Low-Cost Accelerometers: A Machine Learning Approach
title_fullStr Road Surface Anomaly Assessment Using Low-Cost Accelerometers: A Machine Learning Approach
title_full_unstemmed Road Surface Anomaly Assessment Using Low-Cost Accelerometers: A Machine Learning Approach
title_short Road Surface Anomaly Assessment Using Low-Cost Accelerometers: A Machine Learning Approach
title_sort road surface anomaly assessment using low-cost accelerometers: a machine learning approach
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9146565/
https://www.ncbi.nlm.nih.gov/pubmed/35632196
http://dx.doi.org/10.3390/s22103788
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