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| Titel |
Application of ANN to evaluate effective parameters affecting failure load and displacement of RC buildings |
| VerfasserIn |
M. Hakan Arslan |
| Medientyp |
Artikel
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| Sprache |
Englisch
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| ISSN |
1561-8633
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| Digitales Dokument |
URL |
| Erschienen |
In: Natural Hazards and Earth System Science ; 9, no. 3 ; Nr. 9, no. 3 (2009-06-25), S.967-977 |
| Datensatznummer |
250006803
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| Publikation (Nr.) |
 copernicus.org/nhess-9-967-2009.pdf |
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| Zusammenfassung |
| This study investigated the efficiency of an artificial neural network (ANN)
in predicting and determining failure load and failure displacement of multi
story reinforced concrete (RC) buildings. The study modeled a RC building
with four stories and three bays, with a load bearing system composed of
columns and beams. Non-linear static pushover analysis of the key parameters
in change defined in Turkish Earthquake Code (TEC-2007) for columns and
beams was carried out and the capacity curves, failure loads and
displacements were obtained. Totally 720 RC buildings were analyzed
according to the change intervals of the parameters chosen. The input
parameters were selected as longitudinal bar ratio (ρl) of
columns, transverse reinforcement ratio (Asw/sc), axial load level
(N/No), column and beam cross section, strength of concrete (fc)
and the compression bar ratio (ρ'/ρ) on the beam supports.
Data from the nonlinear analysis were assessed with ANN in terms of failure
load and failure displacement. For all outputs, ANN was trained and tested
using of 11 back-propagation methods. All of the ANN models were found to
perform well for both failure loads and displacements. The analyses also
indicated that a considerable portion of existing RC building stock in
Turkey may not meet the safety standards of the Turkish Earthquake Code
(TEC-2007). |
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