Application of ANN to evaluate effective parameters affecting failure load and displacement of RC buildings
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...
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| Format: | Artikel |
| Sprache: | English |
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Copernicus Publications
2009-06-01
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| Schriftenreihe: | Natural Hazards and Earth System Sciences |
| Online Zugang: | http://www.nat-hazards-earth-syst-sci.net/9/967/2009/nhess-9-967-2009.pdf |
| _version_ | 1828494860044206080 |
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| author | M. Hakan Arslan |
| author_facet | M. Hakan Arslan |
| author_sort | M. Hakan Arslan |
| collection | DOAJ |
| description | 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 (&rho;<sub><i>l</i></sub>) of columns, transverse reinforcement ratio (<i>A<sub>sw</sub>/s<sub>c</sub></i>), axial load level (<i>N/N<sub>o</sub></i>), column and beam cross section, strength of concrete (<i>f<sub>c</sub></i>) and the compression bar ratio (&rho;'/&rho;) 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). |
| first_indexed | 2024-12-11T12:03:17Z |
| format | Article |
| id | doaj.art-ab4d0c9472d94e4ea06b5d4524e1a1c2 |
| institution | Directory Open Access Journal |
| issn | 1561-8633 1684-9981 |
| language | English |
| last_indexed | 2024-12-11T12:03:17Z |
| publishDate | 2009-06-01 |
| publisher | Copernicus Publications |
| record_format | Article |
| series | Natural Hazards and Earth System Sciences |
| spelling | doaj.art-ab4d0c9472d94e4ea06b5d4524e1a1c22022-12-22T01:08:01ZengCopernicus PublicationsNatural Hazards and Earth System Sciences1561-86331684-99812009-06-0193967977Application of ANN to evaluate effective parameters affecting failure load and displacement of RC buildingsM. Hakan ArslanThis 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 (&rho;<sub><i>l</i></sub>) of columns, transverse reinforcement ratio (<i>A<sub>sw</sub>/s<sub>c</sub></i>), axial load level (<i>N/N<sub>o</sub></i>), column and beam cross section, strength of concrete (<i>f<sub>c</sub></i>) and the compression bar ratio (&rho;'/&rho;) 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).http://www.nat-hazards-earth-syst-sci.net/9/967/2009/nhess-9-967-2009.pdf |
| spellingShingle | M. Hakan Arslan Application of ANN to evaluate effective parameters affecting failure load and displacement of RC buildings Natural Hazards and Earth System Sciences |
| title | Application of ANN to evaluate effective parameters affecting failure load and displacement of RC buildings |
| title_full | Application of ANN to evaluate effective parameters affecting failure load and displacement of RC buildings |
| title_fullStr | Application of ANN to evaluate effective parameters affecting failure load and displacement of RC buildings |
| title_full_unstemmed | Application of ANN to evaluate effective parameters affecting failure load and displacement of RC buildings |
| title_short | Application of ANN to evaluate effective parameters affecting failure load and displacement of RC buildings |
| title_sort | application of ann to evaluate effective parameters affecting failure load and displacement of rc buildings |
| url | http://www.nat-hazards-earth-syst-sci.net/9/967/2009/nhess-9-967-2009.pdf |
| work_keys_str_mv | AT mhakanarslan applicationofanntoevaluateeffectiveparametersaffectingfailureloadanddisplacementofrcbuildings |