Proposal and Evaluation of Prediction of Pavement Rutting Depth by Recurrent Neural Network

Tomoyuki Okuda; Kouyu Suzuki; Naohiko Kohtake

doi:10.1109/IIAI-AAI.2017.177

Proposal and Evaluation of Prediction of Pavement Rutting Depth by Recurrent Neural Network

Tomoyuki Okuda, Kouyu Suzuki, Naohiko Kohtake

Graduate School of System Design and Management

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Abstract

This paper describes a method of predicting the rutting depth by introducing Adam and dropout into MLP of neural network and GRU of recurrent neural net that can handle time series data. We built a model to predict the current rutting depth from the past rutting 3 years ago. We compared RMSE with the multiple regression model (MLR), which is most frequently used as a regression problem for the time variation of rutting depth. As a result, RMSE decreased in the order of MLR, MLP, GRU. The difference between GRU and RMSE of MLR was about 10%.

Original language	English
Title of host publication	Proceedings - 2017 6th IIAI International Congress on Advanced Applied Informatics, IIAI-AAI 2017
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	1053-1054
Number of pages	2
ISBN (Electronic)	9781538606216
DOIs	https://doi.org/10.1109/IIAI-AAI.2017.177
Publication status	Published - 2017 Nov 15
Event	6th IIAI International Congress on Advanced Applied Informatics, IIAI-AAI 2017 - Hamamatsu, Shizuoka, Japan Duration: 2017 Jul 9 → …

Other

Other	6th IIAI International Congress on Advanced Applied Informatics, IIAI-AAI 2017
Country	Japan
City	Hamamatsu, Shizuoka
Period	17/7/9 → …

Keywords

Machine learning
Pavement condition survey
Recurrent neural network
Road management

ASJC Scopus subject areas

Artificial Intelligence
Computer Networks and Communications
Computer Science Applications
Information Systems
Information Systems and Management

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Proposal and Evaluation of Prediction of Pavement Rutting Depth by Recurrent Neural Network. / Okuda, Tomoyuki; Suzuki, Kouyu; Kohtake, Naohiko.

Proceedings - 2017 6th IIAI International Congress on Advanced Applied Informatics, IIAI-AAI 2017. Institute of Electrical and Electronics Engineers Inc., 2017. p. 1053-1054 8113413.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Okuda, T, Suzuki, K & Kohtake, N 2017, Proposal and Evaluation of Prediction of Pavement Rutting Depth by Recurrent Neural Network. in Proceedings - 2017 6th IIAI International Congress on Advanced Applied Informatics, IIAI-AAI 2017., 8113413, Institute of Electrical and Electronics Engineers Inc., pp. 1053-1054, 6th IIAI International Congress on Advanced Applied Informatics, IIAI-AAI 2017, Hamamatsu, Shizuoka, Japan, 17/7/9. https://doi.org/10.1109/IIAI-AAI.2017.177

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abstract = "This paper describes a method of predicting the rutting depth by introducing Adam and dropout into MLP of neural network and GRU of recurrent neural net that can handle time series data. We built a model to predict the current rutting depth from the past rutting 3 years ago. We compared RMSE with the multiple regression model (MLR), which is most frequently used as a regression problem for the time variation of rutting depth. As a result, RMSE decreased in the order of MLR, MLP, GRU. The difference between GRU and RMSE of MLR was about 10%.",

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