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A Comparative Study Regarding Information Quality of Data Acquisition Methods for Gravel Road Condition Measurement
Linnaeus University, Faculty of Technology, Department of Mechanical Engineering. (Data-driven condition assessment of gravel roads for sustainable maintenance)ORCID iD: 0000-0003-2619-9137
Linnaeus University, Faculty of Technology, Department of Mechanical Engineering. Linnaeus University, Linnaeus Knowledge Environments, Digital Transformations. (Data-driven condition assessment of gravel roads for sustainable maintenance)ORCID iD: 0000-0002-2637-6175
2024 (English)In: 17th WCEAM Proceedings, Springer, 2024, p. 343-355Chapter in book (Refereed)
Sustainable development
SDG 9: Build resilient infrastructure, promote inclusive and sustainable industrialization, and foster innovation, SDG 11: Make cities and human settlements inclusive, safe, resilient, and sustainable
Abstract [en]

Gravel roads connect rural areas to urban centres, providing essential access for residents and entrepreneurs with a higher deterioration rate than paved roads. Maintaining these roads at an acceptable level of service is crucial for efficient and safe transportation of goods and services and improved ride quality. Visual windshield surveys are the primary condition assessment method for gravel roads but are unreliable and susceptible to human judgment errors. Technological advancements, particularly sensor technology, offer opportunities for improved condition data collection and assessment for gravel roads. However, the quality of information obtained for various technologies could vary, affecting the condition assessment output and decision-making. For effective decision-making, high-quality information is required. This paper investigates and compares the information quality of the vehicle vibration response signals collected using an Integrated Circuit Piezoelectric (ICP) and smartphone accelerometers on three (3) gravel roads, RT1, RT2 and RT3. The empirical analysis is based on the hypothesis that the quality of information collected with the Smartphone and ICP accelerometers is comparable. Statistical properties, including estimates of time-varying properties of the signal’s mean square values, power spectral density (PSD) and the instantaneous power for eight (8) adjacent frequency sub-bands of the measurements with the Smartphone and ICP (resampled) accelerometers on gravel roads RT1, RT2 and RT3 were compared. The measurements with the ICP accelerometer yielded higher-quality information than smartphone accelerometers. In conclusion, assessing the gravel road condition using technologies that offer high-quality information for efficient decision-making is essential.

Place, publisher, year, edition, pages
Springer, 2024. p. 343-355
Series
Lecture Notes in Mechanical Engineering
Keywords [en]
Accelerometer, Condition assessment, Frequency Sub-band, Gravel road, Information quality, Smartphone
National Category
Reliability and Maintenance Infrastructure Engineering Other Mechanical Engineering
Research subject
Technology (byts ev till Engineering)
Identifiers
URN: urn:nbn:se:lnu:diva-132840DOI: 10.1007/978-3-031-59042-9_28Scopus ID: 2-s2.0-85206387048ISBN: 978-3-031-59042-9 (electronic)ISBN: 978-3-031-59041-2 (print)OAI: oai:DiVA.org:lnu-132840DiVA, id: diva2:1902441
Projects
Data-driven condition assessment of gravel roads for sustainable maintenanceAvailable from: 2024-10-01 Created: 2024-10-01 Last updated: 2025-03-06Bibliographically approved

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Mbiyana, KeeganKans, Mirka

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