Volume 4, Number 4 (11-2014)                   2014, 4(4): 451-472 | Back to browse issues page

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Fathnejat H, Torkzadeh P, Salajegheh E, Ghiasi R. STRUCTURAL DAMAGE DETECTION BY MODEL UPDATING METHOD BASED ON CASCADE FEED-FORWARD NEURAL NETWORK AS AN EFFICIENT APPROXIMATION MECHANISM. International Journal of Optimization in Civil Engineering. 2014; 4 (4) :451-472
URL: http://ijoce.iust.ac.ir/article-1-187-en.html

Abstract:   (2031 Views)
Vibration based techniques of structural damage detection using model updating method, are computationally expensive for large-scale structures. In this study, after locating precisely the eventual damage of a structure using modal strain energy based index (MSEBI), To efficiently reduce the computational cost of model updating during the optimization process of damage severity detection, the MSEBI of structural elements is evaluated using properly trained cascade feed-forward neural network (CFNN). In order to achieve an appropriate artificial neural network (ANN) model for MSEBI evaluation, a set of feed-forward artificial neural networks which are more suitable for non-linear approximation, are trained. All of these neural networks are tested and the results demonstrate that the CFNN model with log-sigmoid hidden layer transfer function is the most suitable ANN model among these selected ANNs. Moreover, to increase damage severity detection accuracy, the optimization process of damage severity detection is carried out by particle swarm optimization (PSO) whose cost function is constructed based on MSEBI. To validate the proposed solution method, two structural examples with different number of members are presented. The results indicate that after determining the damage location, the proposed solution method for damage severity detection leads to significant reduction of computational time compared to finite element method. Furthermore, engaging PSO algorithm by efficient approximation mechanism of finite element (FE) model, maintains the acceptable accuracy of damage severity detection.
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Type of Study: Research | Subject: Optimal design
Received: 2014/11/18 | Accepted: 2014/11/18 | Published: 2014/11/18

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