DOI QR코드

DOI QR Code

Response surface-based model updating to detect damage on reduced-scale masonry arch bridge

  • Alpaslan, Emre (Civil Engineering Department, Ondokuz Mayis University) ;
  • Karaca, Zeki (Civil Engineering Department, Ondokuz Mayis University)
  • Received : 2021.01.06
  • Accepted : 2021.04.19
  • Published : 2021.07.10

Abstract

Response surface (RS) methods, a combination of mathematical and statistical techniques, have been widely used in design optimization, response prediction, and model validation in structural engineering systems. However, its usage in structural damage identification, especially for historic structures has not been quite common. For this purpose, this study attempts to investigate damage detection in a masonry arch bridge. Within the scope of this, a reduced-scale model of a one span historical masonry arch bridge was built in a laboratory environment. To determine the modal parameters of the reduced-scaled bridge model, operational modal analysis (OMA) was performed under ambient vibrations. Signals originated by sensitive accelerometers were collected to quantify the vibratory response of the reduced-scaled model bridge. The experimental natural frequencies, mode shapes, and damping ratios resulting from these measurements were figured out by using the Enhanced Frequency Domain Decomposition (EFDD) technique. The three-dimensional model of the reduced-scale bridge was created in the ANSYS finite element (FE) software program to expose the analytical dynamic characteristics of the bridge model. The results obtained in the experimental application were compared with those of the finite-element analysis of the bridge model. The calibration of the numeric model was utilized depending on the experimental modal analysis results of the reduced-scale bridge by using the RS method. Design of experiments was constructed by using central composite design, and the RS models were generated by performing the genetic aggregation approach. The optimum results between the experimental and numerical analyses were found by using the RS optimization. Then, regional damages created on the scaled model and the changes of dynamic properties of the damaged case were evaluated. The damage location was approximately identified by using the RS method in the calibrated finite-element model. The results demonstrated that the RS-based FE updating approach is an effective way for damage detection and localization in masonry type structures.

Keywords

Acknowledgement

This study was supported by Ondokuz Mayis University as PYO.MUH.1904.17.009 Scientific Research Project.

References

  1. Alpaslan, E. (2019), "Damage detection of historical masonry bridges with analytical model and experimental techniques", PhD Dissertation, Ondokuz Mayis University, Samsun, Turkey.
  2. ANSYS (2013), Swanson Analysis System, Canonsburg, Pennsylvania.
  3. ARTeMIS Modal 1.5. (2012), Structural Vibration Solution, Denmark.
  4. Bassoli, E., Vincenzi, L., D'Altri, A.M., Miranda, M., Forghieri, M. and Castellazzi, G. (2018), "Ambient vibration-based finite element model updating of an earthquake-damaged masonry tower", Struct. Control Hlth. Monit., 25(5), 1-15. https://doi.org/10.1002/stc.2150.
  5. Bendat, J.S. and Piersol, A.G. (2010), Random Data : Analysis and Measurement Procedures, John Wiley and Sons, Newyork, NY, USA.
  6. Brincker, R. and Zhang, L. (2009), "Frequency domain decomposition revisited", Proceedings of the 3rd International Operational Modal Analysis Conference, Portonovo, Italy, May.
  7. Brincker, R., Zhang, L. and Andersen, P. (2010), "Modal identification from ambient response using frequency domain decomposition", Procededing of IMAC-XVIII: A Conference and Exposition on Structural Dynamics, San Antonio, Texas, USA, February.
  8. Cheng, J., Zhang, J., Cai, C.S. and Xiao, R.C. (2007), "A new approach for solving inverse reliability problems with implicit response functions", Eng. Struct., 29(1), 71-79. https://doi.org/10.1016/j.engstruct.2006.04.005.
  9. Dawari, V. and Vesmawala, G. (2013), "Identification of crack damage in reinforced concrete beams using mode shape based methods", Civil Environ. Res., 3(13), 24-29.
  10. Dey, P., Talukdar, S. and Bordoloi, D.J. (2016), "Multiple-crack identification in a channel section steel beam using a combined response surface methodology and genetic algorithm", Struct. Control Hlth. Monit., 23(6), 938-959. https://doi.org/10.1002/stc.1818.
  11. Fang, S.E. and Perera, R. (2011), "Damage identification by response surface based model updating using D-optimal design", Mech. Syst. Signal Pr., 25(2), 717-733. https://doi.org/10.1016/j.ymssp.2010.07.007.
  12. Feng, Y., Wang, C., Briseghella, B., Fenu, L. and Zordan, T. (2020), "Structural optimization of a steel arch bridge with genetic algorithm", Struct. Eng. Int., 1-10. https://doi.org/10.1080/10168664.2020.1773373.
  13. Gatz, R., Uebersax, M. and Konig, O. (2000), "Structural optimization tool using genetic algorithms and ansys", Proceeding 18. CAD-FEM User's Meeting, Internationale FEM-Technologietage, Graf-Zeppelin-Haus, Friedrichshafen, June.
  14. Gentile, C. and Saisi, A. (2007), "Ambient vibration testing of historic masonry towers for structural identification and damage assessment", Constr. Build. Mater., 21(6), 1311-1321. https://doi.org/10.1016/j.conbuildmat.2006.01.007.
  15. Gunaydin, M., Adanur, S. and Altunisik, A.C. (2018), "Ambient vibration based structural evaluation of reinforced concrete building model", Earthq. Struct., 15(3), 335-350. https://doi.org/10.12989/eas.2018.15.3.335.
  16. Guo, Q.T. and Zhang, L.M. (2004), "Finite element model updating based on response surface methodology", Proceedings of the 22nd International Modal Analysis Conference (IMAC), Dearborn, USA, January.
  17. Hariri- Ardebili, M.A., Seyed-Kolbadi, S.M and Noori, M. (2018), "Response surface method for material uncertainty quantification of infrastructures", Shock Vib., 2018, Article ID 1784203. https://doi.org/10.1155/2018/1784203.
  18. Karaca, Z., Turkeli, E. and Pergel, S. (2017), "Seismic assessment of historical masonry structures: The case of Amasya Tashan", Comput. Concrete, 20(4), 409-418. https://doi.org/10.12989/cac.2017.20.4.409.
  19. Landman, D., Simpson, J., Vicroy, D. and Parker, P. (2007), "Response surface methods for efficient complex aircraft configuration aerodynamic characterization", J. Aircraft, 44(4), 1189-1195. https://doi.org/10.2514/1.24810.
  20. Mottershead, J.E. and Friswell, M.I. (1993), "Model updating in structural dynamics: a survey", J. Sound Vib., 167(2), 347-375. https://doi.org/10.1006/jsvi.1993.1340
  21. Mukhopadhyay, T., Dey, T.K., Chowdhury, R. and Chakrabarti, A. (2015), "Structural damage identification using response surface-based multi-objective optimization: A comparative study", Arab. J. Sci. Eng., 40(4), 1027-1044. https://doi.org/10.1007/s13369-015-1591-3.
  22. Ocak, I. (2008), "Prediction of intact rock's elasticity modulus based on uniaxial compressive strength", Istanbul J. Earth Sci., 21(2), 91-97.
  23. Pastor, M., Binda, M. and Harcarik, T. (2012), "Modal assurance criterion", Procedia Eng., 48, 543-548. https://doi.org/10.1016/j.proeng.2012.09.551.
  24. Ramos, L.F., Roeck, G.D., Lourenco, P.B. and Campos-Costa, A. (2010), "Damage identification on arched masonry structures using ambient and random impact vibrations", Eng. Struct., 32(1), 146-162. https://doi.org/10.1016/j.engstruct.2009.09.002.
  25. Ren, W.X. and Chen, H.B. (2010), "Finite element model updating in structural dynamics by using the response surface method", Eng. Struct., 32(8), 2455-2465. https://doi.org/10.1016/j.engstruct.2010.04.019.
  26. Rytter, A. (1993), "Vibrational based inspection of civil engineering structures", PhD Dissertation, Aalborg University, Denmark.
  27. Sofyan (2015), "Response surface application in vibration-based damaged detection of a railway bridge", Procedia Eng., 125, 1108 -1113. https://doi.org/10.1016/j.proeng.2015.11.131.
  28. Song, M., Yousefianmoghadam, S., Mohammadi, M.E., Moaveni, B., Stavridis, A. and Woog, R.L. (2018), "An application of finite element model updating for damage assessment of a two-story reinforced concrete building and comparison with lidar", Struct. Hlth. Monit., 17(5), 1129-1150. https://doi.org/10.1177/1475921717737970.
  29. Tiachacht, S., Bouazzouni, A., Khatir, S., Wahab, M.A., Behtani, A. and Capozucca, R. (2018), "Damage assessment in structures using combination of a modified Cornwell indicator and genetic algorithm", Eng. Struct., 177, 421-430. https://doi.org/10.1016/j.engstruct.2018.09.070.
  30. Turkeli, E. and Ozturk, H.T. (2017), "Optimum design of partially prestressed concrete beams using Genetic Algorithms", Struct. Eng. Mech., 64(5), 579-589. http://doi.org/10.12989/sem.2017.64.5.579.
  31. Turkeli, E., Karaca, Z. and Ozturk, H.T. (2017), "On the wind and earthquake response of reinforced concrete chimneys" Earthq. Struct., 12(5), 559-567. https://doi.org/10.12989/eas.2017.12.5.559.
  32. Umar, S., Bakharya, N. and Abidin, A.R.Z. (2018), "Response surface methodology for damage detection using frequency and mode shape", Measure., 115, 258-268. https://doi.org/10.1016/j.measurement.2017.10.047.