| Title |
Development of Deep Learning Based Deterioration Prediction Model for the Maintenance Planning of Highway Pavement |
| Authors |
Lee, Yongjun ; Sun, Jongwan ; Lee, Minjae |
| DOI |
http://dx.doi.org/10.6106/KJCEM.2019.20.6.034 |
| Keywords |
Pavement Deterioration Prediction; Deep Learning; Recurrent Neural Network; Long Short-Term Memory; Deep Neural Network |
| 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. |