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Open AccessArticle

Development of the Road Pavement Deterioration Model Based on the Deep Learning Method

Department of Urban Engineering, Hanbat National University, Daejeon 34158, Korea
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Electronics 2020, 9(1), 3; https://doi.org/10.3390/electronics9010003
Received: 1 November 2019 / Revised: 12 December 2019 / Accepted: 15 December 2019 / Published: 18 December 2019
(This article belongs to the Special Issue Electronics and Dynamic Open Innovation)
In Korea, data on pavement conditions, such as cracks, rutting depth, and the international roughness index, are obtained using automatic pavement condition investigation equipment, such as ARAN and KRISS, for the same sections of national highways annually to manage their pavement conditions. This study predicts the deterioration of road pavement by using monitoring data from the Korean National Highway Pavement Management System and a recurrent neural network algorithm. The constructed algorithm predicts the pavement condition index for each section of the road network for one year by learning from the time series data for the preceding 10 years. Because pavement type, traffic load, and environmental characteristics differed by section, the sequence lengths (SQL) necessary to optimize each section were also different. The results of minimizing the root-mean-square error, according to the SQL by section and pavement condition index, showed that the error was reduced by 58.3–68.2% with a SQL value of 1, while pavement deterioration in each section could be predicted with a high coefficient of determination of 0.71–0.87. The accurate prediction of maintenance timing for pavement in this study will help optimize the life cycle of road pavement by increasing its life expectancy and reducing its maintenance budget. View Full-Text
Keywords: deep learning; long short-term memory; sequence lengths; pavement deterioration model; crack; rutting depth; international roughness index deep learning; long short-term memory; sequence lengths; pavement deterioration model; crack; rutting depth; international roughness index
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MDPI and ACS Style

Choi, S.; Do, M. Development of the Road Pavement Deterioration Model Based on the Deep Learning Method. Electronics 2020, 9, 3. https://doi.org/10.3390/electronics9010003

AMA Style

Choi S, Do M. Development of the Road Pavement Deterioration Model Based on the Deep Learning Method. Electronics. 2020; 9(1):3. https://doi.org/10.3390/electronics9010003

Chicago/Turabian Style

Choi, Seunghyun; Do, Myungsik. 2020. "Development of the Road Pavement Deterioration Model Based on the Deep Learning Method" Electronics 9, no. 1: 3. https://doi.org/10.3390/electronics9010003

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