000021614 001__ 21614
000021614 005__ 20170622131255.0
000021614 04107 $$aeng
000021614 046__ $$k2017-06-15
000021614 100__ $$aMorfidis, Konstantinos
000021614 24500 $$aAPPROACH TO PREDICTION OF R/C BUILDINGS’ SEISMIC DAMAGE AS PATTERN RECOGNITION PROBLEM USING ARTIFICIAL NEURAL NETWORKS

000021614 24630 $$n6.$$pComputational Methods in Structural Dynamics and Earhquake Engineering
000021614 260__ $$bNational Technical University of Athens, 2017
000021614 506__ $$arestricted
000021614 520__ $$2eng$$aIn the present paper the investigation of the problem of reinforced concrete (r/c) buildings’ seismic damage prediction is exhibited utilizing Artificial Neural Networks (ANN). More specifically, the problem is formulated and solved in terms of the Pattern Recognition Problem using Multilayer Feedforward Perceptron Networks (MFP). The networks are trained by the implementation of two training algorithms: the Scaled Conjugate Gradient algorithm (SCG) and the Resilient Back-Propagation algorithm (RP). The training data-set is created by means of Nonlinear Time History Analyses (NTHA) of 30 r/c buildings which are subjected to 65 earthquakes. The selected buildings have different heights, structural systems and structural eccentricities, and are designed on the basis of the suggestions of Eurocodes. The damage index which is used to describe the seismic damage state of buildings is the Maximum Interstorey Drift Ratio (MIDR). In the context of the present paper the influence of the number and the combination of seismic parameters which describe the level of impact of seismic excitations on the r/c buildings is also investigated. To this end, 8 different and widely used seismic parameters are utilized. Furthermore, the influence of the number of hidden layers, the number of neurons in the hidden layers, as well as the activation functions of neurons is also examined. The generalization abilities of the optimum configured ANNs are investigated through the assessment of their performance in the case of prediction of seismic damage state of the selected buildings subjected to 16 earthquakes different from the earthquakes which are used in the creation of the training data-set. The most significant conclusion that turned out is that the ANNs can reliably classify the r/c buildings into pre-defined damage classes if they are appropriately configured. More specifically, the parametric investigations prove that the most important factor for the effective ANNs’ configuration is the activation functions of output layer’s neurons (tansig function) as well as the number of the hidden layers (the utilization of two hidden layers leads to better results). As regards the examination of the optimum number and combination of seismic input parameters (using the “Stepwise” sensitivity analysis method) it is proved that are dependent on the utilized training algorithm.

000021614 540__ $$aText je chráněný podle autorského zákona č. 121/2000 Sb.
000021614 653__ $$aSeismic Damage Prediction, Artificial Neural Networks, Existing R/C Buildings, Structural Vulnerability Assessment, Seismic Response of Buildings, Pattern Recognition

000021614 7112_ $$aCOMPDYN 2017 - 6th International Thematic Conference$$cRhodes Island (GR)$$d2017-06-15 / 2017-06-17$$gCOMPDYN2017
000021614 720__ $$aMorfidis, Konstantinos$$iKostinakis, Konstantinos
000021614 8560_ $$ffischerc@itam.cas.cz
000021614 8564_ $$s646372$$uhttps://invenio.itam.cas.cz/record/21614/files/17237.pdf$$yOriginal version of the author's contribution as presented on CD, section: [MS35] Simplified Methodologies and Numerical Tools for the Seismic Risk Mitigation of Buildings: Recent Advances and Open Challenges
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000021614 962__ $$r21500
000021614 980__ $$aPAPER