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SIPINA Induction Graph Method for Seismic Risk Prediction

Authors: B. Selma


The aim of this study is to test the feasibility of SIPINA method to predict the harmfulness parameters controlling the seismic response. The approach developed takes into consideration both the focal depth and the peak ground acceleration. The parameter to determine is displacement. The data used for the learning of this method and analysis nonlinear seismic are described and applied to a class of models damaged to some typical structures of the existing urban infrastructure of Jassy, Romania. The results obtained indicate an influence of the focal depth and the peak ground acceleration on the displacement.

Keywords: Displacement, peak ground acceleration, seism, focal depth, SIPINA method

Digital Object Identifier (DOI):

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[1] Breiman, L., Bagging Predictors. Technical Report No, 421, Department of statistics, University of California, 1994.
[2] Quinlan, J.R., Discovering rules by induction from large collections of examples, Expert Systems in the Microelectronic age, PP 168-201, 1979.
[3] Quinlan, J.R., Learning efficient classification procedures and their applications to chess endgames. In Machine Learning: An artificial Intelligence approach, Volume 1. Morgan Kaufmann, 1983.
[4] D.A. Zighed, J.P. Auray and G.Duru. sipina: Méthode et logiciel. Lacassagne,1992.
[5] Quinlan R., C4.5: Programs for Machine Learning. Morgan Kaufmann Publ., San Mateo, CA, 1993.
[6] M. Hammami, Y. Chahir, L. Chen, D. Zighed, "Détection des régions de couleur de peau dans l’image" revue RIA-ECA vol 17, Ed.Hermès, ISBN 2-7462-0631-5, Janvier 2003, pp.219-231.
[7] M. Hammami, “Modèle de peau et application à la classification d’images et au filtrage des sites web”, Thesis , Ecole Centrale de Lyon, 2005.
[8] B. Atmani, B. Beldjilali, “Knowledge discovery in database: Induction graph and cellular automaton”, Computing and informatics journal, Vol.26, 2007, pp.171-197.
[9] Zighed, D.A.: SIPINA for Windows, ver 2.5. Laboratory ERIC, University of Lyon, 1996.
[10] Rabaseda, S., Zighed, D.A.: Generation and Simplification of Rules in Graphs of Induction. Acts of the 25th Symposium of the Economic Structures, Econometrics and Data Processing, 1996, p. 7.
[11] Rakotomalala, R., Zighed, D.A.: Feschet, Characterization of Production Rules in a Process of Induction. Hermes Science Publication, Paris 1999.
[12] J. R. Quinlan. C4.5: Programs for Machine Learning. Morgan Kaufmann, 1993.
[13] Atanasiu, G.M., Popa, B. F. (2005), Modeling of the seismic action for new concept of performance-based design, Proceedings of the 4th International Conference on Management of Technological Change, Chania, Greece, pp. 153-159.
[14] Popa, B. F. (2006), Elaborating Advanced Methodologies for Evaluating the Dynamic Performance of Reinforced Concrete Structures, PhD Thesis, Technical University of Iasi, Romania.
[15] SAP2000 vs. 9.1 (2005), Integrated Software for Structural Analysis and Design, Computer and Structures Inc.,