Deterioration Prediction of Infrastructures with Time Series Data Considering Long Memory Effect
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Détails bibliographiques
Auteur(s): |
Naoki Kitaura
(Osaka University, Osaka, Japan)
Kiyoyuki Kaito (Osaka University, Osaka, Japan) |
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Médium: | papier de conférence | ||||
Langue(s): | anglais | ||||
Conférence: | IABSE Conference: Structural Engineering: Providing Solutions to Global Challenges, Geneva, Switzerland, September 2015 | ||||
Publié dans: | IABSE Conference Geneva 2015 | ||||
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Page(s): | 961-968 | ||||
Nombre total de pages (du PDF): | 8 | ||||
Année: | 2015 | ||||
DOI: | 10.2749/222137815818358132 | ||||
Abstrait: |
In order to compensate for shortcomings of asset management based on visual inspection data, asset management based monitoring data has got a lot of attention. However, there is little methodology to apply time series data to conduct a decision making on asset management. In addition, long-term monitoring data of infrastructure have long memory effect because the deterioration of gradually progress owing to accumulating various deterioration factors such as traffic load, weathering, anti-freezing agent and etc. In this study, the authors propose ARFIMAX- GARCH (Autoregressive Fractional Integrated Moving average with eXogenous variables- Generalized Autoregressive Conditional Heteroskedaticity) model and attempt to demonstrate the applicability of the proposed model by studying concrete application cases. |