Probabilistic computational mechanics of structures with a ground anchor device: from identification by SHM to reliability assessment of quays

Original Paper
First Online:
Received:
Accepted:
  • 128 Downloads

Abstract

The understanding of the real in-service behavior of complex structures is the first step before modeling. That is still a challenge that requires structural monitoring in conjunction with structural modeling. Moreover, structural reassessment based on reliability updating during in-service conditions is needed for the identification and updating of boundary conditions of the structural model. Polynomial chaos allows providing an automatic treatment of data for monitored structures since it leads, on the one hand, to the decomposition of random variables and their efficient representation when considering maximum likelihood estimate and, on the other hand, to provide a format suitable for direct stochastic finite element analysis. In this paper, we illustrate a reliability updating of two monitored port structures to highlight similarities and specificities: we have chosen two on-piles quays instrumented by the GeM in the west coast of France. One of these quays involves an imperfect mechanical connection. One of the novelties of this paper is to identify the probabilistic properties of this defect. These structures are subjected to complex loads and among them we can underline wind actions. In fact, due to global warming, speed of wind is considerably increasing in some areas and will change the reliability level of some infrastructures. We present at first, from measurements of trajectories of stochastic fields of loads along the quay, an updating of basic variables representing random mechanical parameters of sensible components of the structure. Another novelty of the paper concerns similarities in the distribution of basic random variables which allow us to generalize the probabilistic modeling for other non-instrumented and similar quays, i.e., with the same global design and anchorage technology. Then, a mechanical finite element model of the quays is used as a transfer function of the random variable inputs to model the in-service behavior of the structure during severe conditions of wind action on a lift-crane. Non-intrusive stochastic finite element method is finally selected to evaluate the probability of failure for several scenarios.

Keywords

On-piles quay Soil–structure interaction Monitoring Tie rods Uncertainties Structural reliability Polynomial chaos 
This is a preview of subscription content, log in to check access.

Notes

Acknowledgments

The authors would like to thank Harbor Authorities of Nantes St Nazaire for their technical support and expert judgment.

References

  1. 1.
    Del Grosso A (2000) Monitoring of infrastructures in the marine environment. In: Proceedings of the 3rd international workshop on structural control, Paris, FranceGoogle Scholar
  2. 2.
    Martin NJ, Bell SI (1999) The restoration of Shuaiba oil pier, Kuwait. In: Proceedings of the international conference on monitoring and control of marine and harbour structures, Genoa, ItalyGoogle Scholar
  3. 3.
    Vallone M, Giammarinaro S (2007) Monitoring of Termini Imerese harbour (Sicily, Italy) using satellite DInSAR data. In: Proceedings of the IV international conference applied geophysics for engineering, Messina, ItalyGoogle Scholar
  4. 4.
    Nunez MA, Briançon L, Dias D (2013) Analyses of a pile-supported embankment over soft clay: full-scale experiment, analytical and numerical approaches. Eng Geol 153:53–67CrossRefGoogle Scholar
  5. 5.
    Kim W, Laman JA (2010) Numerical analysis method for long-term behavior of integral abutment bridges. Eng Struct 32(8):2247–2257CrossRefGoogle Scholar
  6. 6.
    Lanata F, Schoefs F (2012) Multi-algorithm approach for identification of structural behavior of complex structures under cyclic environmental loading. Struct Health Monit (SAGE) 11:51–67CrossRefGoogle Scholar
  7. 7.
    Perrin F, Sudret B, Blatman G, Pendola M (2007) Use of polynomial chaos expansion and maximum likelihood estimation for probabilistic inverse problems. In: Proceedings of the 18 Congrès Français de Mécanique, (CFM’2007), Grenoble, FranceGoogle Scholar
  8. 8.
    Adeli H, Jiang X (2008) Intelligent infrastructure: neural networks, wavelets, and chaos theory for intelligent transportation systems and smart structures. CRC Press, Taylor & Francis Group, p 440Google Scholar
  9. 9.
    Yáñez-Godoy H, Schoefs F, Casari P (2008) Statistical analysis of the effects of building conditions on the initial loadings of on-piles quays. J Struct Health Monit 7(3):245–263CrossRefGoogle Scholar
  10. 10.
    Schoefs F, Yáñez-Godoy H, Lanata F (2011) Polynomial chaos representation for identification of mechanical characteristics of instrumented structures. Comput-Aided Civil Infrastruct Eng 26(3):173–189CrossRefGoogle Scholar
  11. 11.
    Yáñez-Godoy H, Schoefs F, Nouy A, Casari P (2006) Extreme storm loading on in-service wharf structures Interest of monitoring for reliability updating. Eur J Environ Civil Eng 10(5):565–581Google Scholar
  12. 12.
    Boéro J, Schoefs F, Yañez-Godoy H, Capra B (2012) Time-function reliability of harbour infrastructures from stochastic modelling of corrosion. Eur J Environ Civil Eng 16(10):1187–1201CrossRefGoogle Scholar
  13. 13.
    Verdure L, Schoefs F, Casari P, Yáñez-Godoy H (2005) Uncertainty updating of an on pile wharf after monitoring. In: Proceedings of the 9th international conference on structural safety and reliability (ICOSSAR 2005), Rome, Italy, pp 1347–1354Google Scholar
  14. 14.
    Schoefs F, Le KT, Lanata F (2013) (2013) Response surface meta-models for the assessment of embankment frictional angle stochastic properties from monitoring: application to harbour structures. Comput Geotech 53:122–132CrossRefGoogle Scholar
  15. 15.
    Verdure L (2004) Cadre statistique du suivi en service des ouvrages de génie civil: application à un quai sur pieux. Ph.D. thesis. Nantes University, FranceGoogle Scholar
  16. 16.
    Yáñez-Godoy H. (2008) Mise à jour de variables aléatoires à partir des données d’instrumentations pour le calcul en fiabilité de structures portuaires. Ph.D. thesis. Nantes University, FranceGoogle Scholar
  17. 17.
    Ghanem R, Spanos PD (1990) Polynomial chaos in stochastic finite elements. J Appl Mech 57(1):197–202MATHCrossRefGoogle Scholar
  18. 18.
    Desceliers C, Soize C, Ghanem R (2007) Identification of chaos representations of elastic properties of random media using experimental vibration tests. Comput Mech 39(6):831–838MATHCrossRefGoogle Scholar
  19. 19.
    CSTB (1995) Traité de physique du bâtiment, Tome 1: connaissances de base. Centre Scientifique et Technique du Bâtiment, FranceGoogle Scholar
  20. 20.
    Berveiller M (2005) Eléments finis stochastiques : approches intrusive et non intrusive pour des analyses de fiabilité. Ph.D. Thesis. Blaise Pascal University, FranceGoogle Scholar
  21. 21.
    Calgaro JA (1996) Introduction aux Eurocodes: sécurité des constructions et bases de la théorie de la fiabilité. Presses de l’École Nationale des Ponts et Chaussées, ParisGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2014

Authors and Affiliations

  1. 1.Bordeaux University, I2M-GCE, UMR CNRS 5295Talence CedexFrance
  2. 2.Faculté des Sciences et des Techniques, Institute in Civil and Mechanical Engineering (GeM)/Sea and Littoral Research Institute (IUML)LUNAM Université, Université de Nantes, CNRS UMR 6183/FR 3473NantesFrance

Personalised recommendations