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Development and field application of a multivariate statistical process control framework for health-monitoring of transportation infrastructure

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  • Chen, Yikai
  • Durango-Cohen, Pablo L.

Abstract

We present a two-part multivariate statistical process control framework to support health-monitoring of transportation infrastructure. The first part consists of estimation of regression and ARIMA–GARCH models to explain, predict, and control for common-cause variation in the data, i.e., changes that can be attributed to usual operating conditions, including traffic loads, environmental effects, and damage when present throughout the data. The second part of the framework consists of using multivariate control charts to simultaneously analyze the standardized innovations of the aforementioned models in order to detect possible special-cause or extraordinary events, such as unique/infrequent traffic, weather, or the onset of damage. The proposed approach revolves around construction of T2 control charts as a framework to jointly monitor the evolution and contemporaneous correlation of a set of measurements. The approach provides significant practical/computational advantages over individual analysis of multiple structural properties, and addresses technical problems stemming from ignoring the relationships among them.

Suggested Citation

  • Chen, Yikai & Durango-Cohen, Pablo L., 2015. "Development and field application of a multivariate statistical process control framework for health-monitoring of transportation infrastructure," Transportation Research Part B: Methodological, Elsevier, vol. 81(P1), pages 78-102.
    • Handle: RePEc:eee:transb:v:81:y:2015:i:p1:p:78-102
    DOI: 10.1016/j.trb.2015.08.012
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    References listed on IDEAS

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    1. Kobayashi, K. & Kaito, K. & Lethanh, N., 2014. "A competing Markov model for cracking prediction on civil structures," Transportation Research Part B: Methodological, Elsevier, vol. 68(C), pages 345-362.
    2. David Ardia, 2008. "Financial Risk Management with Bayesian Estimation of GARCH Models," Lecture Notes in Economics and Mathematical Systems, Springer, number 978-3-540-78657-3, October.
    3. Durango-Cohen, Pablo L., 2007. "A time series analysis framework for transportation infrastructure management," Transportation Research Part B: Methodological, Elsevier, vol. 41(5), pages 493-505, June.
    4. Chu, Chih-Yuan & Durango-Cohen, Pablo L., 2008. "Estimation of dynamic performance models for transportation infrastructure using panel data," Transportation Research Part B: Methodological, Elsevier, vol. 42(1), pages 57-81, January.
    5. Alwan, Layth C & Roberts, Harry V, 1988. "Time-Series Modeling for Statistical Process Control," Journal of Business & Economic Statistics, American Statistical Association, vol. 6(1), pages 87-95, January.
    6. Bersimis, Sotiris & Panaretos, John & Psarakis, Stelios, 2005. "Multivariate Statistical Process Control Charts and the Problem of Interpretation: A Short Overview and Some Applications in Industry," MPRA Paper 6397, University Library of Munich, Germany.
    7. Chen, Yikai & Corr, David J. & Durango-Cohen, Pablo L., 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," Transportation Research Part B: Methodological, Elsevier, vol. 59(C), pages 96-116.
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