Deterioration Prediction of Infrastructures with Time Series Data Considering Long Memory Effect
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説明
<jats:p><p>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.</p></jats:p>
収録刊行物
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- IABSE Reports
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IABSE Reports 105 961-968, 2015-01-01
International Association for Bridge and Structural Engineering (IABSE)