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http://dx.doi.org/10.6106/KJCEM.2019.20.6.034

Development of Deep Learning Based Deterioration Prediction Model for the Maintenance Planning of Highway Pavement  

Lee, Yongjun (Department of Infrastructure Safety Research, Korea Institute of Civil Engineering and Building Technology)
Sun, Jongwan (Department of Infrastructure Safety Research, Korea Institute of Civil Engineering and Building Technology)
Lee, Minjae (Department of Infrastructure Safety Research, Korea Institute of Civil Engineering and Building Technology)
Publication Information
Korean Journal of Construction Engineering and Management / v.20, no.6, 2019 , pp. 34-43 More about this Journal
Abstract
The maintenance cost for road pavement is gradually increasing due to the continuous increase in road extension as well as increase in the number of old routes that have passed the public period. As a result, there is a need for a method of minimizing costs through preventative grievance preventive maintenance requires the establishment of a strategic plan through accurate prediction of road pavement. Hence, In this study, the deep neural network(DNN) and the recurrent neural network(RNN) were used in order to develop the expressway pavement damage prediction model. A superior model among these two network models was then suggested by comparing and analyzing their performance. In order to solve the RNN's vanishing gradient problem, the LSTM (Long short-term memory) circuits which are a more complicated form of the RNN structure were used. The learning result showed that the RMSE value of the RNN-LSTM model was 0.102 which was lower than the RMSE value of the DNN model, indicating that the performance of the RNN-LSTM model was superior. In addition, high accuracy of the RNN-LSTM model was verified through the comparison between the estimated average road pavement condition and the actually measured road pavement condition of the target section over time.
Keywords
Pavement Deterioration Prediction; Deep Learning; Recurrent Neural Network; Long Short-Term Memory; Deep Neural Network;
Citations & Related Records
Times Cited By KSCI : 1  (Citation Analysis)
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1 Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., and Ghemawat, S. (2016). "Tensorflow: Large-scale machine learning on heterogeneous distributed systems."arXiv preprint arXiv:1603.04467.
2 Choi, S. (2018). "Development of Road Asset Management System based on Artificial Intelligence using Visual Information." Doctoral Thesis.
3 Do, M. (2011). "Comparative analysis on mean life reliability with functionally classified pavement sections." International Journal of Highway Engineering, 14(5), pp. 11-19.   DOI
4 Do, M., Lee, Y., Lim, K., and Kwon, S. (2011). "Estimation of Performance and Pavement Life using National Highway Pavement Condition Index." KSCE Journal of Civil Engineering, 15(2), pp. 261-270.   DOI
5 DOMITROVIC, J., DRAGOVAN, H., RUKAVINA, T., and DIMTER, S. (2018). "Application of an Artificial Neural Network in Pavement Management System." Tehnicki vjesnik, 25(2), pp. 466-473.
6 Lee, Y. (2019). "A Study on Construction of Highway Pavement Asset Management System based on Big Data." Doctoral Thesis.
7 Loizos, A., and Karlaftis, M.G. (2005). "Prediction of pavement crack initiation from in-service pavements: A duration model approach." Journal of the Transportation Research Board, 1940, TRB, pp. 38-42.   DOI
8 MOLIT (2011). "Road Pavement Structure Design Manual."
9 Park, J. (2013). "A Study on the Improvement of Business Process Efficient for Expressway Pavement Management." Master Thesis.
10 Han, D., Do, M., and Kim, B. (2017b). "Internal Property and Stochastic Deterioration Modeling of Total Pavement Condition Index for Transportation Asset Management." International journal of highway engineering, 19(5), pp. 1-11.   DOI
11 Park, J. (2018). "Estimation of Electrical Loads Patterns by Usage in the Urban Railway Station by RNN." The Transactions of the Korean Institute of Electrical Engineers, 67(11), pp. 1536-1541.   DOI
12 Suman, S., and Sinha, S. (2012). "Pavement Condition Forecasting Through Artificial Neural Network Modelling." International Journal of Emerging Technology and Advanced Engineering, 2(11), pp. 474-478.
13 Yang, J., Gunaratne, M., Lu, J.J., and Dietrich, B. (2003). "Application of Neural Network Models For Forecating of Pavement Crack Index and Pavement Condition Rating." Florida Department of Transportation.
14 Yang, J., Gunaratne, M., Lu, J.J., and Dietrich, B. (2005). "Use of recurrent Markov chains for modeling the crack performance of flexible pavements." Journal of Transportation Engineering, 131(11), pp. 861-872.   DOI
15 Korea Expressway Corporation (2018). "2017 Investigation and analysis of highway pavement condition."
16 Gharaibeh, N., and Darter, M. (2003). "Probabilistic analysis of highway pavement life for Illinois." Transportation Research Record 1823, No.03-4294, pp. 111-120.   DOI
17 Kobayashi, K., and Do, M. (2010). "Estimation of Markovian transition probabilities for pavement deterioration forecasting." KSCE Journal of Civil Engineering, 14(3), pp. 343-351.   DOI
18 Korea Expressway Corporation (2010). "Guideline for Exposure to Environment."
19 Kwon, S., Jeong, K., and Sun, Y. (2012). "A Study on Decision Criteria of traffic volumes for Choosing of Modified Asphalt Pavement in Korea National Highway." International journal of highway engineering, 4(3), pp. 25-33.
20 Han, D., Yoo, I., and Lee, S. (2017a). "Improvement of Multivariable, Nonlinear, and Overdispersion Modeling with Deep Learning: A Case Study on Prediction of Vehicle Fuel Consumption Rate." Intl. Journal of the Highway Engineers, 19(4), pp. 1-7.
21 Hochreiter, S., and Schmidhuber, J. (1997). "Long Short-Term Memory." Neural Computation archive, 9(8), pp. 1735-1780.   DOI
22 Lee, I., Lee, Y., Park, S., Cho, H., and Lee, M. (2018). "A Study on Utilization of Private Capital for Efficient Highway Pavement Management." Korean Journal of Construction Engineering and Management, KICEM 19(1), pp. 3-11.   DOI
23 Lee, Y. (2013). "A Study on the Method of Establishing Road Maintenance Strategy Considering the Forecasting Traffic Demand." Master Thesis.
24 Lee, Y., and Lee, M. (2016). "A Study on Estimating of Probability Distribution and Mean Life of Bridge Member for Effective Maintenance of the Bridge." Korean Journal of Construction Engineering and Management, 17(4), pp. 57-65.   DOI
25 You, P., and Lee, D. (2002). "Methodology of a Probabilistic Pavement Performance Prediction Model Based on the Markov Process." International Journal of Highway Engineering, 4(4), pp. 1-12.