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Use of genetic optimization in parameter identification of reinforced concrete bridge girders

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Abstract

The real response of a given structure, such as deflection, generally differs from prediction obtained from theoretical models. Hence, the need of periodic or long-term structural monitoring has become mandatory. The present paper describes an approach to localize the most suitable period to perform deflection monitoring in case of reinforced concrete bridges. The searched period allows making the best updates of theoretical models to better represent the structural behaviour with time. So, the genetic optimization is employed to update an existing law of flexural rigidity in order to minimize the difference between the measured and predicted deflections; measurements will be considered from different time periods. The proposed methodology is applied to a representative set of 21 RC T-beam bridges with variable parameters. Simulated measurements of static deflections based on weigh-in-motion data collected in some European countries are used in the application.

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References

  1. Aghagholizadeh M, Catbas FN (2015) A review of model updating methods for civil infrastructure systems. In: Kruis J, Tsompanakis Y, Topping BHV (eds) Computational techniques for civil and structural engineering. Saxe-Coburg Publications, Stirlingshire, pp 83–99

    Chapter  Google Scholar 

  2. Yuen KV, Beck JL, Katafygiotis LS (2006) Unified probabilistic approach for model updating and damage detection. J Appl Mech 73:555. https://doi.org/10.1115/1.2150235

    Article  Google Scholar 

  3. Hajela P, Soeiro F (1989) Structural damage detection based on static and modal analysis. American Institute of Aeronautics and Astronautics, Reston

    Book  Google Scholar 

  4. Sanayei M, Scampoli S (1991) Structural element stiffness identification from static test data. J Eng Mech 117(5):1021–1036

    Article  Google Scholar 

  5. Robert-Nicoud Y, Raphael B, Burdet O, Smith IFC (2005) Model identification of bridges using measurement data. Comput-Aided Civ Infrastruct Eng 20:118–131

    Article  Google Scholar 

  6. Catbas N, Gokce HB, Frangopol DM (2013) Predictive analysis by incorporating uncertainty through a family of models calibrated with structural health-monitoring data. J Eng Mech 139:712–723. https://doi.org/10.1061/(asce)em.1943-7889.0000342

    Article  Google Scholar 

  7. Lewis RM, Torczon V, Trosset MW (2000) Direct search methods: then and now. J Comput Appl Math 124:191–207

    Article  Google Scholar 

  8. Liu GR, Ma WB, Han X (2002) An inverse procedure for determination of material constants of composite laminates using elastic waves. Comput Methods Appl Mech Eng 191:3543–3554

    Article  Google Scholar 

  9. Vishnuvardhan J, Krishnamurthy CV, Balasubramaniam K (2007) Genetic algorithm based reconstruction of the elastic moduli of orthotropic plates using an ultrasonic guided wave single transmitter-multiple-receiver SHM array. Smart Mater Struct 16:1639–1650

    Article  Google Scholar 

  10. Gomes HM, Awruch AM, Lopes PAM (2011) Reliability based optimization of laminated composite structures using genetic algorithms and artificial neural networks. Struct Saf 33:186–195

    Article  Google Scholar 

  11. Tee KF, Khan LR, Chen HP, Alani AM (2014) Reliability based life cycle cost optimization for underground pipeline networks. Tunn Undergr Space Technol 43:32–40

    Article  Google Scholar 

  12. Das SK, Basudhar PK (2006) Comparison study of parameter estimation techniques for rock failure criterion models. Can Geotech J 43:764–771

    Article  Google Scholar 

  13. Das SK, Basudhar PK (2011) Parameter optimization of rock failure criterion using error-in-variables approach. Int J Geomech 11:36–43. https://doi.org/10.1061/(asce)gm.1943-5622.0000069

    Article  Google Scholar 

  14. Mergos PE, Sextos AG (2019) Selection of earthquake ground motions for multiple objectives using genetic algorithms. Eng Struct 187:414–427

    Article  Google Scholar 

  15. Concrete bridge development group history of concrete bridges. http://www.cbdg.org.uk/intro2.asp. Accessed 10 May 2017

  16. Enright B, O’Brien EJ (2013) Monte Carlo simulation of extreme traffic loading on short and medium span bridges. Struct Infrastruct Eng 9:1267–1282. https://doi.org/10.1080/15732479.2012.688753

    Article  Google Scholar 

  17. O’Brien EJ, Caprani CC, O’Connell GJ (2006) Bridge assessment loading: a comparison of West and Central/East Europe. Bridge Struct 2:25–33. https://doi.org/10.1080/15732480600578451

    Article  Google Scholar 

  18. Bruls A, Calgaro JA, Mathieu, H, Prat M (1996) ENV1991—part 3: the main models of traffic Loads on bridges; background studies. In: Proceedings of IABSE Colloquium. IABSE-AIPC-IVBH, Delft, The Netherlands, pp 215–228

  19. Meystre T, Hirt MA (2006) Evaluation de ponts routiers existants avec un modèle de charge de trafic actualisé. Berne

  20. Jacob B, Labry D (2002) Evaluation of the effects of heavy vehicles on bridges fatigue. In: Proceedings 7th international symposium on heavy vehicle weights & dimensions

  21. O’Brien EJ, Enright B (2011) Modeling same-direction two-lane traffic for bridge loading. Struct Saf 33:296–304. https://doi.org/10.1016/j.strusafe.2011.04.004

    Article  Google Scholar 

  22. El Hajj Chehade F, Younes R, Mroueh H, Hage Chehade F (2018) Use of stochastic optimization in the analysis of weigh-in-motion data. In: Powers N et al (eds) Maintenance, safety, risk, management and life-cycle performance of bridges. Taylor & Francis Group, London

    Google Scholar 

  23. Enright B (2010) Simulation of traffic loading on highway bridges. Dublin Institute of Technology, Dublin

    Google Scholar 

  24. Gilbert RI (2001) Shrinkage, cracking and deflection the serviceability of concrete structures. Electron J Struct Eng 1:15–37

    Google Scholar 

  25. Gilbert RI (2011) The serviceability limit states in reinforced concrete design. Procedia Eng 14:385–395. https://doi.org/10.1016/j.proeng.2011.07.048

    Article  Google Scholar 

  26. (1993) CEB-FIP MODEL CODE 1990

  27. Balaguru P, Shah SP (1982) A method of predicting crack widths and deflections for fatigue loading. Spec Publ 75:153–176

    Google Scholar 

  28. Holland JH (1975) Adaptation in natural and artificial systems. University of Michigan Press, Ann Arbor

    Google Scholar 

  29. Deb K (2001) Multi-objective optimization using evolutionary algorithms. Wiley, Chichester

    Google Scholar 

  30. FASCICULE 61 Conception, calcul et épreuves des ouvrages d’art titre II. Programmes de charges et épreuves des ponts-routes

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Authors and Affiliations

  1. Laboratory of Civil Engineering and geo-Environment (LGCgE), Lille 1 University, Villeneuve-d’Ascq, France

    Fatima El Hajj Chehade & Hussein Mroueh

  2. Modelling Center Doctoral School of Science and Technology, Hadath, Lebanon

    Fatima El Hajj Chehade & Fadi Hage Chehade

  3. Faculty of Engineering, Lebanese University, Hadath, Lebanon

    Fatima El Hajj Chehade & Rafic Younes

Authors
  1. Fatima El Hajj Chehade
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  2. Rafic Younes
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  3. Hussein Mroueh
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  4. Fadi Hage Chehade
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Correspondence to Fatima El Hajj Chehade.

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El Hajj Chehade, F., Younes, R., Mroueh, H. et al. Use of genetic optimization in parameter identification of reinforced concrete bridge girders. Innov. Infrastruct. Solut. 5, 89 (2020). https://doi.org/10.1007/s41062-020-00339-2

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  • DOI: https://doi.org/10.1007/s41062-020-00339-2

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