DEEP LEARNING MODEL FOR PREDICTION OF TUNNEL LIGHTING LUMINAIRES DETERIORATION
-
- OKUMURA Naoto
- 愛媛大学大学院 生産環境工学専攻
-
- TSUBOTA Takahiro
- 愛媛大学大学院 生産環境工学専攻
-
- YOSHII Toshio
- 愛媛大学大学院 生産環境工学専攻
Bibliographic Information
- Other Title
-
- AIを用いたトンネル照明灯具の劣化予測モデル
Abstract
<p>This study develops a multilayer neural network model (hereinafter referred to as an "AI model") to predict the deterioration of light fixtures installed in highway tunnels. The AI model is based on the results of an inspection of light fixture installations classified in the "C" category, and uses 15 environmental factors that are considered to affect the degradation rate of light fixtures as inputs, and outputs the progress of degradation at the time of the next inspection. A logistic regression model was also developed. Then, test data that had not been used for training were input to the constructed model to make predictions, and the prediction reproducibility was evaluated. The results showed that the AI model was able to predict lamp deterioration more accurately than the logistic regression model. Furthermore, a sensitivity analysis of the input data was conducted to identify the variables that are important for improving the accuracy of the model.</p>
Journal
-
- Intelligence, Informatics and Infrastructure
-
Intelligence, Informatics and Infrastructure 3 (J2), 1010-1016, 2022
Japan Society of Civil Engineers
- Tweet
Details 詳細情報について
-
- CRID
- 1390294113692183552
-
- ISSN
- 24359262
-
- Text Lang
- ja
-
- Data Source
-
- JaLC
-
- Abstract License Flag
- Disallowed