Neural network based model for seismic assessment of existing RC buildings

The objective of this study is to reveal the sufficiency of neural networks (NN) as a securer, quicker, more robust and reliable method to be used in seismic assessment of existing reinforced concrete buildings. The NN based approach is applied as an alternative method to determine the seismic performance of each existing RC buildings, in terms of damage level. In the application of the NN, a multilayer perceptron (MLP) with a back-propagation (BP) algorithm is employed using a scaled conjugate gradient. NN based model wasd eveloped, trained and tested through a based MATLAB program. The database of this model was developed by using a statistical procedure called P25 method. The NN based model was also proved by verification set constituting of real existing RC buildings exposed to 2003 Bingol earthquake. It is demonstrated that the NN based approach is highly successful and can be used as an alternative method to determine the seismic performance of each existing RC buildings.

[1]  Tarek H. Almusallam,et al.  Prediction of compressive strength of concrete using neural networks , 2012 .

[2]  M. Hakan Arslan,et al.  An evaluation of effective design parameters on earthquake performance of RC buildings using neural networks , 2010 .

[3]  Aysegul Askan,et al.  Probabilistic methods for the estimation of potential seismic damage: Application to reinforced concrete buildings in Turkey , 2010 .

[4]  Naci Caglar,et al.  Neural network based approach for determining the shear strength of circular reinforced concrete columns , 2009 .

[5]  Zekâi Sen.  Rapid visual earthquake hazard evaluation of existing buildings by fuzzy logic modeling , 2010, Expert Syst. Appl..

[6]  Naci Caglar,et al.  Neural networks in 3-dimensional dynamic analysis of reinforced concrete buildings , 2008 .

[7]  Ahmet Yakut,et al.  Preliminary seismic performance assessment procedure for existing RC buildings , 2004 .

[8]  C. R. Arjun,et al.  Neural network estimation of duration of strong ground motion using Japanese earthquake records , 2011 .

[9]  Martin Fodslette Møller,et al.  A scaled conjugate gradient algorithm for fast supervised learning , 1993, Neural Networks.

[10]  A. Chateauneuf,et al.  Evaluation of accidental eccentricity for buildings by artificial neural networks , 2012 .

[11]  Guney Ozcebe,et al.  Prediction of potential damage due to severe earthquakes , 2004 .

[12]  Gaetano Manfredi,et al.  A simulated design procedure for the assessment of seismic capacity of existing reinforced concrete buildings , 2010, Adv. Eng. Softw..