ABSTRACT
In order to compensate for shortcomings of Visual Inspection DataBased Asset Management, Monitoring Data Based Asset Management has got a lot of attention. However, there are few researches to detect abnormalities and extract the progress of deterioration based on long-term monitoring data. In this study, the authers express the time series data obtained through long-term monitoring by the ARMAX-GARCH (Autoregressive Moving average with eXogenous variables-Generalized Autoregressive Conditional Heteroskedaticity) model, and develop the efficient method to estimate unknown parameters based on Bayesian method. Then, a method to forecast the timing of detailed inspection utilizing the ARMAX-GARCH model is developed. Lastly, this methodology is applied to the data of long-term monitoring targeted at the joint members of viaducts, to evaluate its effectiveness.
