Improving Fatigue Evaluations of Structures Using In-Service Behavior Measurement Data
Publication: Journal of Bridge Engineering
Volume 19, Issue 11
Abstract
Conservative models and code practices are usually employed for fatigue-damage predictions of existing structures. Direct in-service behavior measurements are able to provide more accurate estimations of remaining-fatigue-life predictions. However, these estimations are often accurate only for measured locations and measured load conditions. Behavior models are necessary for exploiting information given by measurements and predicting the fatigue damage at all critical locations and for other load cases. Model-prediction accuracy can be improved using system identification techniques where the properties of structures are inferred using behavior measurements. Building upon recent developments in system identification where both model and measurement uncertainties are considered, this paper presents a new data-interpretation framework for reducing uncertainties related to prediction of fatigue life. An initial experimental investigation confirms that, compared with traditional engineering approaches, the methodology provides a safe and more realistic estimation of the fatigue reserve capacity. A second application on a full-scale bridge also confirms that using load-test data reduces the uncertainty related to remaining-fatigue-life predictions.
Get full access to this article
View all available purchase options and get full access to this article.
Acknowledgments
The authors acknowledge ICOM (Steel Structures Laboratory), EPFL for providing test results, bridge drawings, and monitoring data, and Yves Reuland for work on the truss-beam example. This work was partially funded by the Swiss national Science Foundation under contract no. 200020-144304.
References
AASHTO. (2007). LRFD bridge design specifications, 4th Ed., American Association of State Highway and Transportation Officials, Washington, DC.
Acevedo, C., and Nussbaumer, A. (2012). “Effect of tensile residual stresses on fatigue crack growth and curves in tubular joints loaded in compression.” Int. J. Fatigue, 36(1), 171–180.
ASME. (2006). Guide for verification and validation in computational solid mechanics, ASME, New York.
Beven, K. J. (2006). “A manifesto for the equifinality thesis.” J. Hydrol., 320(1–2), 18–36.
Beven, K. J., Smith, P. J., and Freer, J. E. (2008). “So just why would a modeller choose to be incoherent?” J. Hydrol., 354(1–4), 15–32.
Chan, T. H. T., Li, Z. X., and Ko, J. M. (2001). “Fatigue analysis and life prediction of bridges with structural health monitoring data—Part II: Application.” Int. J. Fatigue, 23(1), 55–64.
Cheung, S. H., and Beck, J. L. (2009). “Bayesian model updating using hybrid Monte Carlo simulation with application to structural dynamic models with many uncertain parameters.” J. Eng. Mech., 243–255.
European Committee for Standardization (CEN). (2005). “Design of steel structrures, part 1.9: Fatigue.” Eurocode 3, EN1993-1-9, Brussels.
Fisher, J. W., Kulak, G. L., and Smith, I. F. C. (1998). A fatigue primer for structural engineers, National Steel Bridge Alliance, Chicago.
Goulet, J.-A., Michel, C., and Smith, I. F. C. (2013). “Hybrid probabilities and error-domain structural identification using ambient vibration monitoring.” Mech. Syst. Signal Process., 37(1–2), 199–212.
Goulet, J.-A., and Smith, I. F. C. (2013a). “Predicting the usefulness of monitoring for identifying the behavior of structures.” J. Struct. Eng., 1716–1727.
Goulet, J.-A., and Smith, I. F. C. (2013b). “Structural identification with systematic errors and unknown uncertainty dependencies.” Comp. Struct., 128(Nov.), 251–258.
Guo, T., and Chen, Y.-W. (2013). “Fatigue reliability analysis of steel bridge details based on field-monitored data and linear elastic fracture mechanics.” Struct. Infrastruct. Eng., 9(5), 496–505.
Guo, T., Frangopol, D. M., and Chen, Y.-W. (2012). “Fatigue reliability assessment of steel bridge details integrating weigh-in-motion data and probabilistic finite element analysis.” Comp. Struct., 112–113(Dec.), 245–257.
Hobbacher, A. (2012). “Recommendations for fatigue design of welded joints and components.” Rep. XIII-1965-03, Int. Institute of Welding, Cambridge, U.K.
Joint Committee for Guides in Metrology (JCGM). (2011). “Evaluation of measurement data—Supplement 2 to the “Guide to the expression of uncertainty in measurement”—Extension to any number of output quantities,” JCGM 102:2011, Working Group 1 of the Joint Committee for Guides in Metrology, Sèvres, France.
Liu, M., Frangopol, D. M., and Kwon, K. (2010). “Fatigue reliability assessment of retrofitted steel bridges integrating monitored data.” Struct. Saf., 32(1), 77–89.
MacKay, D. (2003). Information theory, inference, and learning algorithms, Cambridge University Press, Cambridge, U.K.
Orcesi, A. D., and Frangopol, D. M. (2010). “Inclusion of crawl tests and long-term health monitoring in bridge serviceability analysis.” J. Bridge Eng., 312–326.
Papadimitriou, C., Fritzen, C.-P., Kraemer, P., and Ntotsios, E. (2011). “Fatigue predictions in entire body of metallic structures from a limited number of vibration sensors using Kalman filtering.” Struct. Contr. Health Monit., 18(5), 554–573.
Schumacher, A., and Blanc, A. (1999). “Stress measurements and fatigue analysis on the new bridge at Aarwangen.” Rep. No. 386, ICOM, EPFL, Lausanne, Switzerland.
SIA261 Code. (2003a). Norme SIA261: Actions on structures, SIA, Zurich, Switzerland.
SIA263 Code. (2003b). Norme SIA263: Steel structures, SIA, Zurich, Switzerland.
Šidák, Z. (1967). “Rectangular confidence regions for the means of multivariate normal distributions.” J. Am. Stat. Assoc., 62(318), 626–633.
Siriwardane, S., Ohga, M., Dissanayake, R., and Taniwaki, K. (2008). “Application of new damage indicator-based sequential law for remaining fatigue life estimation of railway bridges.” J. Constr. Steel Res., 64(2), 228–237.
Soliman, M., Frangopol, D. M., and Kown, K. (2013). “Fatigue assessment and service life prediction of existing steel bridges by integrating SHM into a probabilistic bilinear approach.” J. Struct. Eng., 1728–1740.
Strauss, A., Frangopol, D. M., and Kim, S. (2008). “Use of monitoring extreme data for the performance prediction of structures: Bayesian updating.” Eng. Struct., 30(12), 3654–3666.
Sweeney, R. A. P. (1976). “The load spectrum for the Fraser River Bridge at New Westminster, BC.” Proc., 75th Technical Conf., March 22–24, 1976, Vol. 77, American Railway Engineering Association, Chicago.
Stahlbau Zentrum Schweiz (SZS). (2005). Konstruktionstabellen: C5/05 steel work, Zurich, Switzerland.
Uzgider, E., Sanli, A. K., Piroglu, F., Ozgen, A., Caglayan, B. O., and Tektunali, A. C. (1996). “Testing and evaluation of Karacam railway bridge.” Rep. No. 5, NATO Science for Stability Programme TU-850-BRIDGES Research Project Report, Istanbul Technical Univ., Civil Engineering Faculty, Structural Dept., Istanbul, Turkey.
Ye, X. W., Ni, Y. Q., Wong, K. Y., and Ko, J. M. (2012). “Statistical analysis of stress spectra for fatigue life assessment of steel bridges with structural health monitoring data.” Eng. Struct., 45(Dec.), 166–176.
Yuen, K. V. (2010). Bayesian methods for structural dynamics and civil engineering, Wiley, New York.
Zhang, E. L., Feissel, P., and Antoni, J. (2011). “A comprehensive Bayesian approach for model updating and quantification of modeling errors.” Probab. Eng. Mech., 26(4), 550–560.
Zhao, X.-L., et al. (2002). Design guide for circular and rectangular hollow section welded joints under fatigue loading, TÜV-Verlag GmbH, Cologne, Germany.
Zhou, Y. E. (2006). “Assessment of bridge remaining fatigue life through field strain measurement.” J. Bridge Eng., 737–744.
Information & Authors
Information
Published In
Journal of Bridge Engineering
Volume 19 • Issue 11 • November 2014
Copyright
© 2014 American Society of Civil Engineers.
History
Received: Aug 22, 2013
Accepted: Feb 28, 2014
Published online: Apr 8, 2014
Discussion open until: Sep 8, 2014
Published in print: Nov 1, 2014
Authors
Metrics & Citations
Metrics
Citations
Download citation
If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download.