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Use of Bayesian updating of time-dependent performance indicators for prediction of structural lifetime and critical inspection points in time

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Abstract
A comprehensive life-cycle performance assessment of structures and infrastructures requires the definition of Performance Indicators (PIs), which provide an easier and time efficient assessment procedure. It is a tool by which the safety and integrity of the structural system is ensured by preventing the PIs from crossing their thresholds. While structures are subjected to time-dependent degradation processes which require consideration of uncertainties, the evolution of PIs can be simulated and forecasted with appropriate statistical model in combination with available inspection and monitoring information. An innovative Bayesian framework for making use of the available historical data and incorporation of additional information from inspection and monitoring in a so-called Stepwise Changing Rate Updating Method (SCRUM) is developed in this paper to predict the future PIs. Specific to the approach is the use of the Changing Rate (CR) between subsequent PI observations. First, an estimation of the statistical properties of the PI as well as its CR is made, and then further inspection and monitoring allows for incorporating additional information in the Bayesian updating framework for performance prediction. A time-dependent weight factor is defined and implemented in order to account for the decreasing influence of previous CRs on the predicted PI. The advantage of SCRUM is that neither an underlying mechanical nor dynamic model is needed to do the forecasting, making it an easy applicable tool which only uses Monte Carlo simulations to fulfill its task. The primary objective is to determine the lifetime of the structure, given a clear definition of the lifetime and the failure probability limit. Furthermore, the developed method can be used for the detection of optimal inspection intervals. The application of SCRUM is limited to stable processes, i.e. processes for which the performance indicator decreases almost monotonic.
Keywords
MONITORING EXTREME DATA

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MLA
Xing, Cheng, et al. “Use of Bayesian Updating of Time-Dependent Performance Indicators for Prediction of Structural Lifetime and Critical Inspection Points in Time.” Life-Cycle of Engineering Systems: Emphasis on Sustainable Civil Infrastructure: Proceedings of the Fifth International Symposium on Life-Cycle Civil Engineering (IALCCE 2016), 16-19 October 2016, Delft, The Netherlands, edited by Jaap Bakker et al., CRC PRESS-BALKEMA, 2017, pp. 448–55.
APA
Xing, C., Van Kerkhove, M., Caspeele, R., & Taerwe, L. (2017). Use of Bayesian updating of time-dependent performance indicators for prediction of structural lifetime and critical inspection points in time. In J. Bakker, D. M. Frangopol, & K. van Breugel (Eds.), Life-Cycle of Engineering Systems: Emphasis on Sustainable Civil Infrastructure: Proceedings of the Fifth International Symposium on Life-Cycle Civil Engineering (IALCCE 2016), 16-19 October 2016, Delft, The Netherlands (pp. 448–455). LEIDEN: CRC PRESS-BALKEMA.
Chicago author-date
Xing, Cheng, Matthias Van Kerkhove, Robby Caspeele, and Luc Taerwe. 2017. “Use of Bayesian Updating of Time-Dependent Performance Indicators for Prediction of Structural Lifetime and Critical Inspection Points in Time.” In Life-Cycle of Engineering Systems: Emphasis on Sustainable Civil Infrastructure: Proceedings of the Fifth International Symposium on Life-Cycle Civil Engineering (IALCCE 2016), 16-19 October 2016, Delft, The Netherlands, edited by Jaap Bakker, Dan M. Frangopol, and Klaas van Breugel, 448–55. LEIDEN: CRC PRESS-BALKEMA.
Chicago author-date (all authors)
Xing, Cheng, Matthias Van Kerkhove, Robby Caspeele, and Luc Taerwe. 2017. “Use of Bayesian Updating of Time-Dependent Performance Indicators for Prediction of Structural Lifetime and Critical Inspection Points in Time.” In Life-Cycle of Engineering Systems: Emphasis on Sustainable Civil Infrastructure: Proceedings of the Fifth International Symposium on Life-Cycle Civil Engineering (IALCCE 2016), 16-19 October 2016, Delft, The Netherlands, ed by. Jaap Bakker, Dan M. Frangopol, and Klaas van Breugel, 448–455. LEIDEN: CRC PRESS-BALKEMA.
Vancouver
1.
Xing C, Van Kerkhove M, Caspeele R, Taerwe L. Use of Bayesian updating of time-dependent performance indicators for prediction of structural lifetime and critical inspection points in time. In: Bakker J, Frangopol DM, van Breugel K, editors. Life-Cycle of Engineering Systems: Emphasis on Sustainable Civil Infrastructure: Proceedings of the Fifth International Symposium on Life-Cycle Civil Engineering (IALCCE 2016), 16-19 October 2016, Delft, The Netherlands. LEIDEN: CRC PRESS-BALKEMA; 2017. p. 448–55.
IEEE
[1]
C. Xing, M. Van Kerkhove, R. Caspeele, and L. Taerwe, “Use of Bayesian updating of time-dependent performance indicators for prediction of structural lifetime and critical inspection points in time,” in Life-Cycle of Engineering Systems: Emphasis on Sustainable Civil Infrastructure: Proceedings of the Fifth International Symposium on Life-Cycle Civil Engineering (IALCCE 2016), 16-19 October 2016, Delft, The Netherlands, Delft, 2017, pp. 448–455.
@inproceedings{8136359,
  abstract     = {{A comprehensive life-cycle performance assessment of structures and infrastructures requires the definition of Performance Indicators (PIs), which provide an easier and time efficient assessment procedure. It is a tool by which the safety and integrity of the structural system is ensured by preventing the PIs from crossing their thresholds. While structures are subjected to time-dependent degradation processes which require consideration of uncertainties, the evolution of PIs can be simulated and forecasted with appropriate statistical model in combination with available inspection and monitoring information. An innovative Bayesian
framework for making use of the available historical data and incorporation of additional information from
inspection and monitoring in a so-called Stepwise Changing Rate Updating Method (SCRUM) is developed
in this paper to predict the future PIs. Specific to the approach is the use of the Changing Rate (CR) between
subsequent PI observations. First, an estimation of the statistical properties of the PI as well as its CR is made,
and then further inspection and monitoring allows for incorporating additional information in the Bayesian
updating framework for performance prediction. A time-dependent weight factor is defined and implemented
in order to account for the decreasing influence of previous CRs on the predicted PI. The advantage of
SCRUM is that neither an underlying mechanical nor dynamic model is needed to do the forecasting, making
it an easy applicable tool which only uses Monte Carlo simulations to fulfill its task. The primary objective is
to determine the lifetime of the structure, given a clear definition of the lifetime and the failure probability
limit. Furthermore, the developed method can be used for the detection of optimal inspection intervals. The
application of SCRUM is limited to stable processes, i.e. processes for which the performance indicator decreases
almost monotonic.}},
  author       = {{Xing, Cheng and Van Kerkhove, Matthias and Caspeele, Robby and Taerwe, Luc}},
  booktitle    = {{Life-Cycle of Engineering Systems: Emphasis on Sustainable Civil Infrastructure: Proceedings of the Fifth International Symposium on Life-Cycle Civil Engineering (IALCCE 2016), 16-19 October 2016, Delft, The Netherlands}},
  editor       = {{Bakker, Jaap and Frangopol, Dan M. and van Breugel, Klaas}},
  isbn         = {{9781138028470}},
  keywords     = {{MONITORING EXTREME DATA}},
  language     = {{eng}},
  location     = {{Delft}},
  pages        = {{448--455}},
  publisher    = {{CRC PRESS-BALKEMA}},
  title        = {{Use of Bayesian updating of time-dependent performance indicators for prediction of structural lifetime and critical inspection points in time}},
  url          = {{https://doi.org/10.1201/9781315375175}},
  year         = {{2017}},
}

Web of Science
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