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Computers and Concrete
  Volume 12, Number 2, August 2013 , pages 229-241

Neural network based model for seismic assessment of existing RC buildings
Naci Caglar and Zehra Sule Garip

    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.
Key Words
    neural networks; scaled conjugate gradient algorithm; rapid assessment; P25 method; existing RC buildings
Naci Caglar: Department of Civil Engineering, Sakarya University, Esentepe Campus, Sakarya, Turkey
Zehra Sule Garip: Department of Civil Engineering, Karabuk University, Karabuk, Turkey

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