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Nonlinear Prediction Surfaces for Estimating the Structural Response of Naval Vessels

  • Alysson Mondoro
  • Mohamed Soliman
  • Dan M. Frangopol
Conference paper
Part of the Conference Proceedings of the Society for Experimental Mechanics Series book series (CPSEMS)

Abstract

Structural health monitoring (SHM) of naval vessels is essential for assessing the performance of the structure and the fatigue damage accrued over the service life. The direct integration of available SHM data may be useful in reducing the epistemic uncertainties arising from inaccuracies in the modeling and the variations in the as-built structural configuration from the initial design. Based on SHM data, fatigue damage indices can be predicted by implementing cell based approaches, such as the lifetime weighted sea method, that discretizes the operational conditions of the vessel into cells with specific wave height, heading angle, and speed. The integration of SHM data into the fatigue assessment using lifetime weighted sea method requires a complete set of data that covers the whole operational spectrum. However, technical malfunctions or discrete monitoring practices generate incomplete data sets. This paper proposes nonlinear prediction surfaces to estimate the ship structural response in unobserved cells based on available cell data. Expected theoretical variations of the structural response to changes in wave height, heading angle, and vessel speed are integrated in the development of the prediction surface. The proposed methodology is illustrated on the SHM data from a high speed aluminum catamaran.

Keywords

Fatigue Aluminum vessels Structural health monitoring Missing data Nonlinear prediction 

Notes

Acknowledgments

The support by grants from (a) the National Science Foundation (NSF) Award CMMI-1537926, (b) the U.S. Office of Naval Research (ONR) Awards N00014-08-1-0188, N00014-12-1-0023, and N00014-16-1-2299, and (c) the National Aeronautics and Space Administration (NASA) Award NNX10AJ20G is gratefully acknowledged. The opinions presented in this paper are those of the authors and do not necessarily reflect the views of the sponsoring organizations.

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Copyright information

© The Society for Experimental Mechanics, Inc. 2017

Authors and Affiliations

  • Alysson Mondoro
    • 1
  • Mohamed Soliman
    • 2
  • Dan M. Frangopol
    • 1
  1. 1.Department of Civil and Environmental Engineering, ATLSS Engineering Research CenterLehigh UniversityBethlehemUSA
  2. 2.School of Civil and Environmental Engineering, College of Engineering, Architecture and Technology, Oklahoma State UniversityStillwaterUSA

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