Pavement Distress Recognition via Wavelet-Based Clustering of Smartphone Accelerometer Data
Publication: Journal of Computing in Civil Engineering
Volume 36, Issue 4
Abstract
The ubiquity of smartphones has led to a variety of studies on how phone accelerometers can be used to assess road pavement quality. The majority of prior studies have emphasized supervised machine learning, assuming the ability to collect labeled data for model development. However, variances in vehicle dynamics and roadway quality, as well as the reliance on data labeling, limit the generalizability, scalability, and reproducibility of these approaches. Here, we propose an unsupervised learning framework that combines Pareto-optimized wavelet featurization and clustering. We first demonstrate the applicability of wavelets as features for dimensionally reduced accelerometer data. Wavelet featurization typically requires significant empirical tuning for optimal featurization. The presented framework automates tuning and selection of wavelet features based on subsequent clustering metrics. These metrics are related to inherent cluster characteristics such as intercluster variance and between-cluster variance. Rather than select a clustering method a priori, the proposed approach optimizes across a variety of clustering algorithms and hyperparameter configurations, again based on a set of clustering metrics. Experimental evaluation shows that the framework is able to detect road pavement distress and distinguish between classes of pavement defects, but that low-cost smartphone sensor data may not be as reliable in discriminating the more nuanced characteristics of pavement distress. This was most notable in the case of cracking, where the type and range of cracking severity caused undesirable cluster separation. The presented framework is general and cost-efficient and opens the way to further research on automatic pavement distress recognition from crowdsourced, low-cost data.
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Data Availability Statement
The data and code generated and used in this paper are available from the corresponding author upon reasonable request.
Acknowledgments
This work was sponsored by a grant from the Center for Integrated Asset Management for Multimodal Transportation Infrastructure Systems (CIAMTIS), a US Department of Transportation University Transportation Center, under federal Grant No. 69A3551847103. The contents of this work reflect the views of the authors, who are responsible for the facts and the accuracy of the information presented herein. This document is disseminated in the interest of information exchange. The US Government assumes no liability for the contents or use thereof. The authors are grateful for the support.
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Journal of Computing in Civil Engineering
Volume 36 • Issue 4 • July 2022
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© 2022 American Society of Civil Engineers.
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Received: Aug 30, 2021
Accepted: Jan 10, 2022
Published online: Mar 16, 2022
Published in print: Jul 1, 2022
Discussion open until: Aug 16, 2022
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- P. Shivananda, Sangram Kumar Khatua, Study on International Road Roughness index (IRI) using Smart phone application from REVA University to Kodigehalli gate, Bangalore, India, IOP Conference Series: Materials Science and Engineering, 10.1088/1757-899X/1255/1/012020, 1255, 1, (012020), (2022).