10 Reliability Quantification of High-Speed Naval Vessels Based on SHM Data 105 Fig. 10.5 Time-variant system reliability index at various speeds considering sagging, hogging, and fatigue failure are identified and a peak extraction algorithm is applied to find the histograms of the strain peaks for sagging and hogging. Extreme value statistics are used to find the parameters of the extreme value distribution of the global effects. For fatigue reliability evaluation, the stress range bin histograms were built using the SHM data to provide an estimate of the equivalent stress range and the average number of cycles of the detail at a given sea condition. The methodology was illustrated on the naval HSV-2 strain data obtained from the SHM information recorded during the seakeeping trails of the ship. As expected, it was shown that the speed of the ship significantly affects the reliability. It was also observed that fatigue becomes the dominant failure mode after few years of the service life. This number of years can be clearly found by plotting the reliability index profiles. Using these profiles and by setting the appropriate threshold, the reliability-based service life can be obtained. Furthermore, an integrated approach can be used to evaluate the overall ship reliability under its full expected operational profile. The approach presented in this paper reveals the advantages of using SHM information for quantifying uncertainties associated with the performance evaluation of ship structures. Acknowledgements The support of the U.S. Office of Naval Research (Awards numbers N00014-08-1-0188 and N00014-12-1-0023, Structural Reliability Program, Director Dr. Paul E. Hess III, ONR, Code 331) is gratefully acknowledged. The opinions and conclusions presented in this paper are those of the writers and do not necessarily reflect the views of the sponsoring organization. References 1. Kwon K, Frangopol DM (2012) Fatigue life assessment and lifetime management of aluminum ships using life-cycle optimization. J Ship Res 56:91–105 2. Frangopol DM, Bocchini P, Deco A, Kim S, Kwon K, Okasha NM, Saydam D (2012) Integrated life-cycle framework for maintenance, monitoring, and reliability of naval ship structures. Nav Eng J 124(1):89–99 3. Salvino LW, Brady TF (2008) Hull monitoring system development using hierarchical framework for data and information management. In: Proceedings of the 7th international conference on computer and IT applications in the maritime industry 4. Kwon K, Frangopol DM, Kim S (2013) Fatigue performance assessment and lifetime prediction of high-speed ship structures based on probabilistic lifetime sea loads. Struct Infrastruct Eng 9:102–115 5. Frangopol DM (2011) Life-cycle performance, management, and optimization of structural systems under uncertainty: accomplishments and challenges. Struct Infrastruct Eng 7:389–413 6. Kim S, Frangopol DM, Soliman M (2013) Generalized probabilistic framework for optimum inspection and maintenance planning. J Struct Eng 139:435–447 7. Kim S, Frangopol DM (2011) Optimum inspection planning for minimizing fatigue damage detection delay of ship hull structures. Int J Fatigue 33:448–459 8. Okasha NM, Frangopol DM, Saydam D, Salvino LW (2011) Reliability analysis and damage detection in high-speed naval craft based on structural health monitoring data. Struct Health Monit 10:361–379 9. Fisher JW (1984) Fatigue and fracture in steel bridges, case studies. Wiley, New York 10. Barsom JM, Rolfe ST (1999) Fracture and fatigue control in structures: applications of fracture mechanics. ASTM International, West Conshohocken, PA 11. Eurocode 3 (2010) Design of steel structures part 1–9, fatigue strength. CEN—European committee for Standardisation
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