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http://dx.doi.org/10.12815/kits.2022.21.5.133

Development of Deterioration Model for Cracks in Asphalt Pavement Using Deep Learning-Based Road Asset Monitoring System  

Park, Jeong-Gwon (Korea Land and Housing Corporation)
Kim, Chang-Hak (Dept. of Civil Engineering, Gyeonsang National University)
Choi, Seung-Hyun (Dept. of Urban Engineering, Hanbat National University)
Do, Myung-Sik (Dept. of Urban Engineering, Hanbat National University)
Publication Information
The Journal of The Korea Institute of Intelligent Transport Systems / v.21, no.5, 2022 , pp. 133-148 More about this Journal
Abstract
In this study, a road pavement crack deterioration model was developed for a pavement road sections of the Sejong-city. Data required for model development were acquired using a deep learning-based road asset monitoring system. Road pavement monitoring was conducted on the same sections in 2021 and 2022. The developed model was analyzed by dividing it into a method for estimating the annual average amount of deterioration and a method based on Bayesian Markov Mixture Hazard model. As a result of the analysis, it was found that an analysis results similar to the crack deterioration model developed based on the data acquired from the Automatic pavement investigation equipmen was derived. The results of this study are expected to be used as basic data by local governments to establish road management plans.
Keywords
Asset management; Crack; Pavement deterioration model; Bayesian markov mixture hazard;
Citations & Related Records
Times Cited By KSCI : 3  (Citation Analysis)
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