Abstract
Risk, as the product of failure probability and failure consequence, has been estimated and applied by engineers and managers to help make critical decisions on (a) maintenance of aging plants, and (b) planning of new infrastructure. For aging plants, failure probabilities are more difficult to estimate than consequences, primarily because of a shortage of time-varying data on the condition of the complex systems of hardware and software at varying scales after years of service. A different argument holds for yet-to-be-built infrastructure, since it is also hard to estimate the time-varying nature of future loadings and resource availability. A dynamic, or, time-dependent risk analysis using a time-varying failure probability and a consequence with uncertainty estimation is an appropriate way to manage aging infrastructure and plan new ones. In this paper, we first introduce the notion of a time-varying failure probability via a numerical example of a multi-scale fatigue model of a steel pipe, and then the concept of a dynamic risk for decision-making via an application of the analysis to the inspection strategy for a cooling piping system of a 40-year-old nuclear power plant. Significance and limitations of the multi-scale fatigue life model and the risk analysis methodology are presented and discussed.
Contribution of National Institute of Standards and Technology. Not subject to copyright.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Kitagawa, H., & Suzuki, I. (1975). Reliability approach in structural engineering. In Freudenthal A. M (Eds.) (pp. 217–233). Tokyo, Japan: Marizen.
Fong, J. T., Rehm, R. G., & Graminski, E. L. (1977). Journal of the Technical Associate of the Pulp and Paper Industry (Vol. 60, p. 156).
Fong, J. T. (1979). Statistical aspects of fatigue at microscopic, specimen, and component Levels. In Fong J. T. (Ed.), Fatigue Mechanisms, Proceedings of an ASTM-NBS-NSF Symposium, Kansas City, Mo., May 1978 ASTM STP 675. American Society for Testing and Materials (pp. 729–758).
Nisitani, et al. (1981) Engineering Fracture Mechanics, 15, 445–456.
Nelson, P. R., Coffin, M., and Copeland, K. A. F. (2006). Introductory statistics for engineering experimentation. Elsevier.
Prochan, F. (1953). Confidence and tolerance intervals for the normal distribution. Journal of the American Statistical Association, 48, 550–564.
Natrella, M. G., 1966, Experimental Statistics, National Bur. of Standards. Handbook 91 (Aug. 1, 1963, reprinted with corrections Oct. 1966), pp 1–14, 1–15, 2–13 to 2–15, Tables A-6 and A-7. Wash., DC 20402: Superintendent of Documents, U.S. Govt. Printing Office (1966).
Dowling, N. E. (1999). Mechanical behavior of materials (2nd ed.). Prentice-Hall.
Dowling, N. E. (1973). Fatigue life and inelastic strain response under complex histories for an alloy steel. Journal of Testing and Evaluation, ASTM, 1(4), 271–287.
Draper, N. R., & Smith, H. (1966). Applied Regression Analysis, Chap. 1–3, pp. 1–103, and Chap. 10, pp. 263–304. Wiley.
Filliben, J. J., & Heckert, N. A. (2002). DATAPLOT: a statistical data analysis software system, National Institute of Standards & Technology, Gaithersburg, MD 20899, http://www.itl.nist.gov/div898/software/dataplot.htm. Â
Croarkin, C., Guthrie, W., Heckert, N. A., Filliben, J. J., Tobias, P., Prins, J., et al. (Eds.) (2003). NIST/SEMATECH e-handbook of statistical methods, http://www.itl.nist.gov/div898/handbook/. First issued, June 1, 2003, and last updated July 18, 2006. Produced jointly by the Statistical Engineering Division of the National Institute of Standards & Technology, Gaithersburg, MD, and the Statistical Methods Group of SEMITECH, Austin, TX. Also available as a NIST Interagency Report in a CD-ROM upon request to alan.heckert@nist.gov.
Ku, H. H. (1966). Notes on the use of propagation of error formulas. Journal of Research of the National Bureau of Standards, 70C(4), 263–273.
Fong, J. T., Heckert, N. A., Filliben, J. J., Marcal, P. V., & Rainsberger, R. (2015). Uncertainty of FEM solutions using a nonlinear least squares fit method and a design of experiments approach. In Proceeding of COMSOL Users’ Conference on Oct. 7–9, 2015. Boston, MA, www.comsol.com/ed/direct/conf/conference2015papers/papers/.
Fong, J. T., Heckert, N. A., Filliben, J. J., Marcal, P. V., Rainsberger, R., & Ma, L. (2015). Uncertainty quantification of stresses in a cracked pipe elbow weldment using a logistic function fit, a nonlinear least squares algorithm, and a super-parametric method. Procedia Engineering, 130, 135–149. Available online at www.sciencedirect.com.
Wilkins, D. J. (2002). The bathtub curve and product failure behavior, part two—normal life and wear-out. Reliability Hot Wire, Issue 22, an online publication by ReliaSoft Corp (2002), https://www.weibull.com/hotwire/issue22/hottopics22.htm.
Harter, H. L. (1977). A survey of the literature on the size effect on material strength. Report AFFDL TR-77-11. Wright-Patterson AFB, Ohio: Air Force Flight Dynamics Laboratory.
Fong, J. T., Heckert, N. A., Filliben, J. J., Marcal, P. V., & Freiman, S. W. (2018). Estimating with uncertainty quantification a minimum design allowable strength for a full-scale component or structure of engineering materials. Manuscript Submitted to a Technical Journal.
Fong, J. T., Heckert, N. A., Filliben, J. J., & Ziehl, P. H. (2018). A nonlinear least squares logistic fit approach to quantifying uncertainty in fatigue stress-life models and an application to plain concrete. In Proceedings of ASME PVP Division Conference July 15–20, 2018, Prague, Czech Republic, Paper No. PVP2018-84739. New York, NY: The American Society of Mechanical Engineers, http://www.asmeconferences.org/PVP2018.
Disclaimer
Certain commercial equipment, instruments, materials, or computer software are identified in this paper in order to specify the experimental or computational procedure adequately. Such identification is not intended to imply recommendation or endorsement by the U.S. National Institute of Standards and Technology, nor it is intended to imply that the materials, equipment, or software identified are necessarily the best available for the purpose.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 This is a U.S. government work and not under copyright protection in the U.S.; foreign copyright protection may apply
About this chapter
Cite this chapter
Fong, J.T., Filliben, J.J., Heckert, N.A., Leber, D.D., Berkman, P.A., Chapman, R.E. (2019). Uncertainty Quantification of Failure Probability and a Dynamic Risk Analysis of Decision-Making for Maintenance of Aging Infrastructure. In: Varde, P., Prakash, R., Joshi, N. (eds) Risk Based Technologies. Springer, Singapore. https://doi.org/10.1007/978-981-13-5796-1_5
Download citation
DOI: https://doi.org/10.1007/978-981-13-5796-1_5
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-13-5795-4
Online ISBN: 978-981-13-5796-1
eBook Packages: EngineeringEngineering (R0)