To compensate for shortcomings of visual inspection data- based asset management, monitoring data-based asset management has attracted 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 authors express the time series data obtained through long-term monitoring by the autoregressive moving average with exogenous variables generalized autoregressive conditional heteroskedasticity (ARMAX-GARCH) 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 viaduct, to evaluate its effectiveness.
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Bollerslev T (1986) Generalized autoregressive conditional heteroskedasticity. J Econometr 31:307–327
Chen Y, Corr DJ, Durango CP (2014) Analysis of common-cause and special-cause variation in the deterioration of transportation infrastructure: a field application of statistical process control for structural health monitoring. Transport Res Part B: Methodol 59(1):96–116
Chib S, Greenberg E (1994) Bayesian inference in regression models with ARMA\((p, q)\) errors. J Econometr 64:183–206
Chu CY, Durango CP (2007) Estimation of infrastructure performance models using state-space specifications of time series models. Transport Res Part C: Emerg Technol 15(1):174–188
Chu CY, Durango CP (2008) Estimation of dynamic performance models for transportation infrastructure using panel data. Transport Res Part B: Methodol 42(1):57–81
Friswell M, Mottershead J (1995) Finite element model updating in structural dynamics. Kluwer Academic Publishers, Amsterdam
Fritzen CP (2005) Vibration-based structural health monitoring–concepts and applications. Key Eng Mater 3(20):293–294
Hastings WK (1970) Monte Carlo sampling methods using Markov chains and their applications. Biometrika 57:97–109
Ibrahim JG, Chen MH, Sinha D (2001) Bayesian survival analysis. Springer, New York
Kaito K, Matsuoka K, Sakai Y, Kawakami J, Arakawa T, Kanagawa M, Kobayashi K (2010) Road-to-vehicle wireless communication monitoring aiming at application for asset management. J Appl Mech Jpn Soc Civil Eng 13:1017–1028
Kaito K, Yasuda K, Kobayashi K, Owada K (2005) Optimal maintenance strategies of bridge components with an average cost minimizing principles. J Jpn Soc Civil Eng No.801/I-73:83–96
Kleibergen F, Van Dijk HK (1993) Non-stationarity in GARCH models: a Batesian analysis. J Appl Econometr 8:S41–S61
Kobayashi K (2005) Decentralized life-cycle cost evaluation and aggregated efficiency. J Jpn Soc Civil Eng No.793/IV-68:59–71
Metropolis N, Rosenbluth AW, Rosenbluth MN, Teller AH, Teller E (1953) Equations of state calculations by fast computing machines. J Chem Phys 21:1087–1092
Nakatsuma T (2000) Bayesian analysis of ARMA-GARCH models: a Markov chain sampling approach. J Econometr 95:57–69
Obama K, Okada K, Kaito K, Kobayashi K (2008) Disaggregated hazard rates evaluation and bench-marking. J Jpn Soc Civil Eng, Ser. A1 (Struct Eng Earthq Eng) 64(4):857–874
Tsuda Y, Kaito K, Aoki K, Kobayashi K (2005) Estimating Markovian transition probabilities for bridge deterioration forecasting. J Jpn Soc Civil Eng No.801/I-73:69–82
Tsurumi H, Radchenko (2005) Relationship between foreign exchange after financial crisis in Asia (unit root co-integration, VAR). Bayesian econometrics model analysis. Toyo Economic Publishers, pp 101–126
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Kobayashi, K., Kaito, K. & Kazumi, K. Deterioration forecasting of joint members based on long-term monitoring data. EURO J Transp Logist 4, 5–30 (2015). https://doi.org/10.1007/s13676-014-0069-x