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Deterioration Prediction of Infrastructures with Time Series Data Considering Long Memory Effect

 Deterioration Prediction of Infrastructures with Time Series Data Considering Long Memory Effect
Auteur(s): ,
Présenté pendant IABSE Conference: Structural Engineering: Providing Solutions to Global Challenges, Geneva, Switzerland, September 2015, publié dans , pp. 961-968
DOI: 10.2749/222137815818358132
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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 ...
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Détails bibliographiques

Auteur(s): (Osaka University, Osaka, Japan)
(Osaka University, Osaka, Japan)
Médium: papier de conférence
Langue(s): anglais
Conférence: IABSE Conference: Structural Engineering: Providing Solutions to Global Challenges, Geneva, Switzerland, September 2015
Publié dans:
Page(s): 961-968 Nombre total de pages (du PDF): 8
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.