Earthquakes and Structures

  • 제21권4호
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  • Pages.413-423
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  • 2021
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  • 2092-7614(pISSN)
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  • 2092-7622(eISSN)
테크노프레스 (Techno-Press)
권호 표지
DOI QR코드

DOI QR Code

Post-disaster damage assessment of structures by neural networks

  • NOURA, Hichem (University Djilali Bounaama, Khemis Miliana, Laboratory of Acoustic and Civil Engineering) ;
  • ABED, Mohamed (University Saad Dahled Blida) ;
  • MEBARKI, Ahmed (University Gustave Eiffel, Laboratory Multi Scale Modeling and Simulation (MSME UMR 8208 UGE/UPEC/CNRS))
  • 투고 : 2021.02.21
  • 심사 : 2021.08.30
  • 발행 : 2021.10.25
⟨ 이전 논문 다음 논문 ⟩

초록

Recent papers have investigated the contribution of structural and secondary elements in the overall damage of structures due to seismic effects. The present paper improves such methods by investigating also the marginal contribution of the geotechnical disorders and geometric regularity, in addition to the combined effect of structural and secondary elements. An adapted artificial neural networks (ANNs) method is proposed for this purpose. In this approach, three groups of parameters are considered for the quantitative evaluation of post- earthquake damage of structures: the structural group, the secondary group and a qualitatively evaluated group consisting observed geotechnical disorders and building regularity. Principal-component analysis is used in order to evaluate the effects of each input variable on the global structural damage. The ANN model is trained and validated for a collected database corresponding to 27,601 of buildings (Boumerdes earthquake, Algeria: M=6.8; May 21, 2003) and tested for 1,000 damaged buildings, located near the hypocentral zone (Bordj-El Bahri city, near Algiers), inspected during a post-quake damage survey. The assessment of the overall damage of structures based on the whole combination of three groups indicates that the developed model provides more accurate theoretical global damage predictions (98% accordance) that render it a promising tool for the inspector to decide about the final damage category.

키워드

과제정보

The authors are grateful the Algerian institutions and their scientific staff, in particular Centre for Earthquakes Engineering Studies (CGS, Algeria) who offered access to the whole database.

참고문헌

  1. Akkouche, K., Hannachi, N.E., Hamizi, M., Khelil, N., Djouzi, K. and Daoui, M. (2019), "Knowledge-based system for damage assessment after earthquake: Algerian buildings case", Asian J. Civil Eng., 20(6), 769-784. https://doi.org/10.1007/s42107-019-00143-z.
  2. Allali, S. A., Abed, M. and Mebarki, A. (2018), "Post-earthquake assessment of buildings damage using fuzzy logic", Eng. Struct., 166, 117-127. https://doi.org/10.1016/j.engstruct.2018.03.055.
  3. Anderson, C.V. (2002), The Federal Emergency Management Agency (FEMA), Nova Science Publishers, Hauppauge, U.S.A.
  4. Anilkumar, S. and Banerji, H. (2021), "An inquiry into success factors for post-disaster housing reconstruction projects: A case of Kerala, South India", Int. J. Dis. Risk Sci., 12(1), 24-39. https://doi.org/10.1007/s13753-020-00309-3.
  5. Belazougui, M. (2008), "Boumerdes Algeria Earthquake of May 21, 2003: Damage analysis and behavior of beam-column reinforced concrete structures", Proceedings of the 14 World Conference on Earthquake Engineering, Beijing, China, October.
  6. Bernal, G.A., Salgado-Galvez, M.A., Zuloaga, D., Tristancho, J., Gonzalez, D. and Cardona, O.D. (2017), "Integration of probabilistic and multi-hazard risk assessment within urban development planning and emergency preparedness and response: application to Manizales, Colombia", Int. J. Dis. Risk Sci., 8(3), 270-283. https://doi.org/10.1007/s13753-017-0135-8.
  7. Boughaba, A. and Bouabaz, M. (2020), "Identification and risk management related to construction projects", Advan. Comput. Des., 5(4), 445-465. https://doi.org/10.12989/ACD.2020.5.4.445.
  8. Boukri, M., Farsi, M.N., Mebarki, A., Belazougui, M., Amellal, O., Mezazigh, B. and Benhamouche, A. (2014), "Seismic risk and damage prediction: case of the buildings in Constantine city (Algeria)", Bull. Earthq. Eng., 12(6), 2683-2704. https://doi.org/10.1007/s10518-014-9594-0.
  9. Cicek, K. and Sar, A. (2020), "A novel risk assessment approach for data center structures", Earthq. Struct., 19(6), 471. http://dx.doi.org/10.12989/eas.2020.19.6.471.
  10. Derbal, I., Bourahla, N., Mebarki, A. and Bahar, R. (2020), "Neural network-based prediction of ground time history responses", Europe. J. Environ. Civil Eng., 24(1), 123-140. https://doi.org/10.1080/19648189.2017.1367727.
  11. Fan, Y., Wen, Q., Wang, W., Wang, P., Li, L. and Zhang, P. (2017), "Quantifying disaster physical damage using remote sensing data-A technical work flow and case study of the 2014 Ludian earthquake in China", Int. J. Dis. Risk Sci., 8(4), 471-488. https://doi.org/10.1007/s13753-017-0143-8.
  12. Foti, D., Giannoccaro, N.I., Greco, P., Lerna, M., Paolicelli, R. and Vacca, V. (2020), "Evaluation of structural operativity of two strategic buildings through Seismic Model", Earthq. Struct., 19(1), 45-57. https://doi.org/10.12989/EAS.2020.19.1.45.
  13. Ge, Y., Gu, Y. and Deng, W. (2010), "Evaluating china's national post-disaster plans: The 2008 Wenchuan Earthquake's recovery and reconstruction planning", Int. J. Dis. Risk Sci., 1(2), 17-27. https://doi.org/10.3974/j.issn.2095-0055.2010.02.003.
  14. Ghahremani, B., Bitaraf, M., Ghorbani-Tanha, A.K. and Fallahi, R. (2021), "Structural damage identification based on fast S-transform and convolutional neural networks", Struct., 29, 1199-1209. https://doi.org/10.1016/j.istruc.2020.11.068.
  15. Gunthal, G., Musson, R.M.W., Schwarz, J. and Stucchi, M. (1993), "The European microseismic scale (MSK-92)", Terra Nova, 5(3), 305-305. https://doi.org/10.1111/j.1365-3121.1993.tb00261.x.
  16. Harirchian, E. and Lahmer, T. (2020), "Developing a hierarchical type-2 fuzzy logic model to improve rapid evaluation of earthquake hazard safety of existing buildings", Struct., 28, 1384-1399. https://doi.org/10.1016/j.istruc.2020.09.048.
  17. Jordan, M. (1995), Why the Logic Function? A Tutorial Discussion on Probabilities and Neural Networks, Technical Report 9503, Computational Cognitive Science, Massachusetts Institute of Technology, Massachusetts, Cambridge.
  18. Kang, H.S. and Kim, Y.T. (2016), "The physical vulnerability of different types of building structure to debris flow events", Nat. Haz., 80(3), 1475-1493. https://doi.org/10.1007/s11069-015-2032-z.
  19. Kaveh, A. and Maniat, M. (2014), "Damage detection in skeletal structures based on charged system search optimization using incomplete modal data", IJCE, 12(2), 193-200. http://ijce.iust.ac.ir/article-1-980-en.html.
  20. Kaya, Z. (2016), "Predicting liquefaction-induced lateral spreading by using neural network and neuro-fuzzy techniques", Int. J. Geomech., 16(4), 04015095. https://doi.org/10.1061/(ASCE)GM.1943-5622.0000607.
  21. Li, M., Hong, M. and Zhang, R. (2018), "Improved Bayesian network-based risk model and its application in disaster risk assessment", Int. J. Dis. Risk Sci., 9(2), 237-248. https://doi.org/10.1007/s13753-018-0171-z.
  22. Marino, S., Cattari, S., Lagomarsino, S., Dizhur, D. and Ingham, J. M. (2019), "Post-earthquake damage simulation of two colonial unreinforced clay brick masonry buildings using the equivalent frame approach", Struct., 19, 212-226. https://doi.org/10.1016/j.istruc.2019.01.010.
  23. Marquardt, D.W. (1963), "An algorithm for least-squares estimation of nonlinear parameters", J. Soc. Ind. Appl. Mathem., 11(2), 431-441. http://www.jstor.org/stable/2098941. https://doi.org/10.1137/0111030
  24. McCulloch, W.S. and Pitts, W. (1943), "A logical calculus of the ideas immanent in nervous activity", Bull. Mathem. Biophy., 5(4), 115-133. https://doi.org/10.1007/BF02478259.
  25. Mebarki, A., Boukri, M., Laribi, A., Farsi, M., Belazougui, M. and Kharchi, F. (2014), "Seismic vulnerability: theory and application to Algerian buildings", J. Seismol., 18(2), 331-343. https://doi.org/10.1007/s10950-013-9377-0.
  26. Mehrotra, A., Singh, K.K., Nigam, M.J. and Pal, K. (2015), "Detection of tsunami-induced changes using generalized improved fuzzy radial basis function neural network", Nat. Haz., 77(1), 367-381. https://doi.org/10.1007/s11069-015-1595-z.
  27. Mitrani-Resier, J., Wu, S. and Beck, J.L. (2016), "Virtual inspector and its application to immediate pre-event and post-event earthquake loss and safety assessment of buildings", Nat. Haz., 81(3), 1861-1878. https://doi.org/10.1007/s11069-016-2159-6.
  28. More, J. (1978), "The Levenberg-Marquardt algorithm: Implementation and theory", Energy Res. Develop. Admin., 630, 105-116. https://doi.org/10.1007/BFb0067700.
  29. Noura, H., Mebarki, A., and Abed, M. (2019), "Post-earthquake structural damage evaluation by neural networks: theory and calibration", Europe. J. Environ. Civil Eng., 23(6), 710-727. https://doi.org/10.1080/19648189.2017.1304277.
  30. Rafi, M.M., Lodi, S.H., Ahmed, M. and Alam, N. (2015), "Observed damages in Pakistan due to 16 April 2013 Iran earthquake", Bull. Earthq. Eng., 13(2), 703-724. https://doi.org/10.1007/s10518-014-9638-5.
  31. Reyes, J., and Cardenas, V. H. (2010), "A Chilean seismic regionalization through a Kohonen neural network", Neural Computing and Applications, 19(7), 1081-1087. https://doi.org/10.1007/s00521-010-0373-9.
  32. Riedel, I., Gueguen, P., Dalla Mura, M., Pathier, E., Leduc, T. and Chanussot, J. (2015), "Seismic vulnerability assessment of urban environments in moderate-to-low seismic hazard regions using association rule learning and support vector machine methods", Nat. Haz., 76(2), 1111-1141. https://doi.org/10.1007/s11069-014-1538-0.
  33. Shao, Y.W., Wu, Y.S., Kao, S.F., Huang, C.J. and Chang, K.Y. (2014), "Application of fuzzy theory on earthquake damage rate estimation of buildings", J. Central South Univ., 21(6), 2454-2459. https://doi.org/10.1007/s11771-014-2199-6.
  34. Swingler, K. (1996), Applying Neural Networks: A Practical Guide, Morgan Kaufmann, Academic Press, London, U.K.
  35. Wong, H.T. and Li, S. (2016), "Healthcare services demand in post-disaster settings: The 2014 earthquake in Ludian County, Yunnan Province, China", Int. J. Dis. Risk Sci., 7(4), 445-449. https://doi.org/10.1007/s13753-016-0107-4.
  36. Xue, X. and Yang, X. (2013), "Application of the adaptive neuro-fuzzy inference system for prediction of soil liquefaction", Nat. Haz., 67(2), 901-917. https://doi.org/10.1007/s11069-013-0615-0.
  37. Yamazaki, F. and Matsuoka, M. (2007), "Remote sensing technologies in post-disaster damage assessment", J. Earthq. Tsunami, 1(03), 193-210. https://doi.org/10.1142/S1793431107000122.
  38. Yazdanian, M., Ingham, J., Kahanek, C. and Dizhur, D. (2020), "Damage assessment of catwalks following the 2016 Kaikoura earthquake", Struct., 28, 1977-1990. https://doi.org/10.1016/j.istruc.2020.10.041.
  39. Zhan, Y., Lu, S., Xiang, T. and Wei, T. (2021), "Application of convolutional neural network in random structural damage identification", Struct., 29, 570-576. https://doi.org/10.1016/j.istruc.2020.11.056.
  40. Zheng, J., Wang, Y., Zhang, K. and Liang, J. (2020), "A dynamic emergency decision-making method based on group decision making with uncertainty information", Int. J. Dis. Risk Sci., 11(5), 667-679. https://doi.org/10.1007/s13753-020-00308-4.