000021517 001__ 21517
000021517 005__ 20170622131249.0
000021517 04107 $$aeng
000021517 046__ $$k2017-06-15
000021517 100__ $$aZadeh, Mostafa Allam
000021517 24500 $$aESTIMATION OF MAXIMUM PEAK GROUND ACCELERATION VIA THE ANFIS AND RBF NEURAL NETWORKS

000021517 24630 $$n6.$$pComputational Methods in Structural Dynamics and Earhquake Engineering
000021517 260__ $$bNational Technical University of Athens, 2017
000021517 506__ $$arestricted
000021517 520__ $$2eng$$aPeak ground acceleration is one of the most important factors which need to be investigated in order to predict the devastation potential resulting from earthquakes in reconstruction sites. Also, the maximum level of shaking control and the maximum load to which a structure is subjected to are criteria that can be worth considering. In this research, a training algorithm based on gradient descent (Traingd) and Levenberg-Marquart (Train LM) were developed and employed by using strong ground motion records. The ANN algorithm indicated that the fitting between the predicted PGA values by the networks and the observed PGA values were able to yield high correlation coefficients of 0.78 for PGA. We attempt to provide a suitable prediction of the large acceleration peak from ground gravity acceleration (1g) in different areas. Methods are defined by using fuzzy inference systems based on adaptive networks, feed-forward neural networks by four basic parameters as input variables which influence an earthquake in an area. The affected indices of an earthquake include the moment magnitude, rupture distance, fault mechanism and site class. The ANFIS network –– with an average error of 0.012 –– is a more precise network than FFBP neural networks. The FFBP network has a mean square error of 0.017 accordingly. Nonetheless, these two networks can have a suitable estimation of probable acceleration peaks (PGA) with levels higher than 1g in the area.

000021517 540__ $$aText je chráněný podle autorského zákona č. 121/2000 Sb.
000021517 653__ $$aAdaptive-Network-based fuzzy inference systems, feed-forward back propagation error of a neural network, peak ground acceleration, rupture distance, site class ,.

000021517 7112_ $$aCOMPDYN 2017 - 6th International Thematic Conference$$cRhodes Island (GR)$$d2017-06-15 / 2017-06-17$$gCOMPDYN2017
000021517 720__ $$aZadeh, Mostafa Allam$$iNasrollahnejad, Ali$$iDoloiee, Gholam Javan
000021517 8560_ $$ffischerc@itam.cas.cz
000021517 8564_ $$s2627622$$uhttps://invenio.itam.cas.cz/record/21517/files/16812.pdf$$yOriginal version of the author's contribution as presented on CD, section: [MS17] Computational issues in earthquake engineering
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000021517 962__ $$r21500
000021517 980__ $$aPAPER