Accomplished Reliability Level for Seismic Structural Damage Prediction Using Artificial Neural Networks

  • Magdalini TyrtaiouEmail author
  • Antonios Papaleonidas
  • Anaxagoras Elenas
  • Lazaros Iliadis
Conference paper
Part of the Proceedings of the International Neural Networks Society book series (INNS, volume 2)


This research aims to determine the optimal Multi-Layer Feed-Forward Artificial Neural Network (MLFF) capable of accurately estimating the level of seismic damage on buildings, by considering a set of Seismic Intensity Parameters (SIP). Twenty SIP (well established and highly correlated to the structural damage) were utilized. Their corresponding values were calculated for a set of seismic signals. Various combinations of at least five seismic features were performed for the development of the input dataset. A vast number of Artificial Neural Networks (ANNs) were developed and tested. Their output was the level of earthquake Damage on a Reinforced Concrete Frame construction (DRCF) as it is expressed by the Park and Ang overall damage index. The potential contribution of nine distinct Machine Learning functions towards the development of the most robust ANN was also investigated. The results confirm that MLFF networks can provide an accurate estimation of the structural damage caused by an earthquake excitation. Hence, they can be considered as a reliable Computational Intelligence approach for the determination of structures’ seismic vulnerability.


Seismic intensity parameters Multiple feedforward perceptron artificial neural network ANN Park and Ang damage index 


  1. 1.
    Alvanitopoulos, P.F., Andreadis, I., Elenas, A.: A genetic algorithm for the classification of earthquake damages in buildings. In: Proceedings of 5th IFIP Conference on Artificial Intelligence Applications and Innovations, Thessaloniki, pp. 341–346 (2009). Scholar
  2. 2.
    Alvanitopoulos, P.F., Andreadis, I., Elenas, A.: A new algorithm for the classification of earthquake damages in structures. In: Proceedings of the 5th IASTED Conference on Signal Processing, Pattern Recognition and Applications, Innsbruck, pp. 151–156 (2008)Google Scholar
  3. 3.
    Alvanitopoulos, P.F., Andreadis, I., Elenas, A.: Neuro-fuzzy techniques for the classification of earthquake damages in buildings. Measurement 43(6), 797–809 (2010)CrossRefGoogle Scholar
  4. 4.
    Araya, R., Saragoni, G.R.: Earthquake accelerogram destructiveness potential factor. In: Proceedings of the 8th World Conference on Earthquake Engineering, pp. 835–842. EERI, El Cerrito (1984)Google Scholar
  5. 5.
    Arias, A.: A measure of earthquake intensity. In: Hansen, R.J. (ed.) Seismic Design for Nuclear Power Plants, pp. 438–483. MIT Press, Cambridge (1970)Google Scholar
  6. 6.
    ATC 3–06 Publication: Tentative Provisions for the Development of Seismic Regulations for Buildings. US Government Printing Office, Washington, DC (1978)Google Scholar
  7. 7.
    Cabãnas, L., Benito, B., Herráiz, M.: An approach to the measurement of the potential structural damage of earthquake ground motions. Earthq. Eng. Struct. Dyn. 26(1), 79–92 (1997)CrossRefGoogle Scholar
  8. 8.
    Chopra, A.K.: Dynamics of Structures. Prentice-Hall, Englewood Cliffs (1995)zbMATHGoogle Scholar
  9. 9.
    Elenas, A., Meskouris, K.: Correlation study between seismic acceleration parameters and damage indices of structures. Eng. Struct. 23(6), 698–704 (2001)CrossRefGoogle Scholar
  10. 10.
    Elenas, A.: Correlation between seismic acceleration parameters and overall structural damage indices of buildings. Soil Dyn. Earthq. Eng. 20(1), 93–100 (2000)CrossRefGoogle Scholar
  11. 11.
    Elenas, A.: Interdependency between seismic acceleration parameters and the behavior of structures. Soil Dyn. Earthq. Eng. 16(5), 317–322 (1997)CrossRefGoogle Scholar
  12. 12.
    Elenas, A.: Seismic-parameter-based statistical procedure for the approximate assessment of structural damage. Math. Probl. Eng. 2014, 1–22 (2014). Article Id 916820Google Scholar
  13. 13.
    Eurocode 2: Design of Concrete Structures—Part 1: General Rules and Rules for Buildings. European Committee for Standardization, Brussels, Belgium (2000)Google Scholar
  14. 14.
    Eurocode 8: Design of Structures for Earthquake Resistance—Part 1: General Rules, Seismic Actions, and Rules for Buildings. European Committee for Standardization, Brussels, Belgium (2004) Google Scholar
  15. 15.
    Fajfar, P., Vidic, T., Fischinger, M.: A measure of earthquake motion capacity to damage medium-period structures. Soil Dyn. Earthq. Eng. 9(5), 236–242 (1990)CrossRefGoogle Scholar
  16. 16.
    Haykin, S.: Neural Netw, 2nd edn. Prentice Hall, Upper Saddle River (1999)zbMATHGoogle Scholar
  17. 17.
    Jennings, P.C.: Engineering seismology. In: Kanamori, H., Boschi, E. (eds.) Earthquakes: Observation, Theory and Interpretation, pp. 138–173. Italian Physical Society, Varenna (1982)Google Scholar
  18. 18.
    Kappos, A.J.: Sensitivity of calculated inelastic seismic response to input motion characteristics. In: Proceedings of the 4th U.S. National Conference on Earthquake Engineering, pp. 25–34. EERI, Oakland (1990)Google Scholar
  19. 19.
    Lautour, O.R., Omenzetter, P.: Prediction of seismic-induced structural damage using artificial neural networks. Eng. Struct. 31(2), 600–606 (2009)CrossRefGoogle Scholar
  20. 20.
    Martinez-Rueda, J.E.: Scaling procedure for natural accelerograms based on a system of spectrum intensity scales. Earthq. Spectra 14(1), 135–152 (1998)CrossRefGoogle Scholar
  21. 21.
    Meskouris, K., Krätzig, W.B., Hanskötter, U.: Nonlinear computer simulations of seismically excited wall-stiffened reinforced concrete buildings. In: Proceedings of the 2nd International Conference on Structural Dynamics (EURODYN’93), Balkema, Lisse, pp. 49–54 (1993)Google Scholar
  22. 22.
    Meskouris, K.: Structural Dynamics: Models, Methods. Examples. Ernst & Sohn, Berlin (2000)Google Scholar
  23. 23.
    Morfidis, K., Kostinakis, K.: Approaches to the rapid seismic damage prediction of r/c buildings using artificial neural networks. Eng. Struct. 165, 120–141 (2018)CrossRefGoogle Scholar
  24. 24.
    Nanos, N., Elenas, A., Ponterosso, P.: Correlation of different strong motion duration parameters and damage indicators of reinforced concrete structures. In: Proceedings of the 14th World Conference on Earthquake Engineering (2008)Google Scholar
  25. 25.
    Park, Y.J., Ang, A.H.-S., Wen, Y.K.: Damage-limiting aseismic design of buildings. Earthq. Spectra 3(1), 1–26 (1987)CrossRefGoogle Scholar
  26. 26.
    Ang, A.H.-S., Park, Y.J., Ang, A.H.-S.: Mechanistic seismic damage model for reinforced concrete. J. Struct. Eng. 111(4), 722–739 (1985)CrossRefGoogle Scholar
  27. 27.
    Reinhorn, A.M., Roh, H., Sivaselvan, M., et al.: IDARC2D version 7.0: a program for the inelastic damage analysis of structures. Technical report MCEER-09-0006, MCEER, State University of New York, Buffalo, NY, USA (2009)Google Scholar
  28. 28.
    Singh, A., Saxena, P., Lalwani, S.: A study of various training algorithms on neural network for angle based triangular problem. Int. J. Comput. Appl. 71(13), 0975–8887 (2013)Google Scholar
  29. 29.
    The Math Works Inc.: MATLAB and Statistics Toolbox Release 2016b. Natick, Massachusetts, USA (2016)Google Scholar
  30. 30.
    Trifunac, M.D., Brady, A.G.: A study on the duration of strong earthquake ground motion. Bull. Seismol. Soc. Am. 65(3), 581–626 (1975)Google Scholar
  31. 31.
    Tsou, P., Shen, M.H.: Structural damage detection and identification using neural network. In: Proceedings of the 34thAIAA/ASME/ASCEAHS/ASC, Structural, Structural Dynamics and Materials Conference, AIAA/ASME Adaptive Structural Forum, pt. 5 (1993)Google Scholar
  32. 32.
    Uang, C.M., Bertero, V.V.: Evaluation of seismic energy in structures. Earthq. Eng. Struct. Dynam. 19(1), 77–90 (1990)CrossRefGoogle Scholar
  33. 33.
    Vanmarcke, E.H., Lai, S.S.P.: Strong-motion duration and RMS amplitude of earthquake records. Bull. Seismol. Soc. Am. 70(4), 1293–1307 (1980)Google Scholar
  34. 34.
    Vui, V.C., Hamid, R.R.: Correlation between seismic parameters of far-fault motions and damage indices of low-rise reinforced concrete frames. Soil Dyn. Earthq. Eng. 66(11), 102–112 (2014)Google Scholar
  35. 35.
    Wu, X., Ghaboussi, J., Garrett, J.: Use of neural network in detection of structural damage. Comput. Struct. 42(4), 649–659 (1992)zbMATHCrossRefGoogle Scholar
  36. 36.
    Zhao, J., Ivan, J.N., DeWold, J.T.: Structural damage detection using artificial neural networks. J. Infrastructural Syst. 13(3), 182–189 (1998)Google Scholar

Copyright information

© The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Department of Civil Engineering, Institute of Structural Statics and DynamicsDemocritus University of ThraceXanthiGreece

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