Estimation of curvature and displacement ductility in reinforced concrete buildings
Abstract Ensuring sufficient ductility in building load bearing systems and elements of the load bearing system is quite important for their seismic performance. The Seismic Codes stipulate that certain requirements must be met to maintain ductility values above a certain level. The purpose of this...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Arslan, M. Hakan [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2012 |
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| Schlagwörter: |
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| Anmerkung: |
© Korean Society of Civil Engineers and Springer-Verlag Berlin Heidelberg 2012 |
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| Übergeordnetes Werk: |
Enthalten in: KSCE journal of civil engineering - Seoul : Korean Soc. of Civil Engineers, 1997, 16(2012), 5 vom: 29. Juni, Seite 759-770 |
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| Übergeordnetes Werk: |
volume:16 ; year:2012 ; number:5 ; day:29 ; month:06 ; pages:759-770 |
| Links: |
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| DOI / URN: |
10.1007/s12205-012-0958-1 |
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SPR02525846X |
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| 100 | 1 | |a Arslan, M. Hakan |e verfasserin |4 aut | |
| 245 | 1 | 0 | |a Estimation of curvature and displacement ductility in reinforced concrete buildings |
| 264 | 1 | |c 2012 | |
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| 520 | |a Abstract Ensuring sufficient ductility in building load bearing systems and elements of the load bearing system is quite important for their seismic performance. The Seismic Codes stipulate that certain requirements must be met to maintain ductility values above a certain level. The purpose of this study is to determine how ductility values of both elements and load bearing systems vary as parameters related to the conditions specified in the codes change and as estimates of these values are used. With this aim in mind, the curvature ductility in columns and beams of a four-storey Reinforced Concrete (RC) building differs depending on parameters that include the axial load level, longitudinal reinforcement, transverse reinforcement, compression bar ratio and concrete strength. The value of the curvature ductility was found to vary according to the number of parameters and variance range, which was found to be 60 and 135 in the beam section and column section, respectively. Later, a pushover analysis was applied to 540 different statuses of the sample RC system for the same parameters, and the ratio variations and respective displacement (global) ductility of the frames were calculated. The relationship between obtained ductility values with the parameters, as well as the accuracy of the established model, were estimated using regression analyses (Multi-linear and Nonlinear Regression (MLR, NLR)) and 11 various Artificial Neural Networks (ANN) methods. According to the estimation methods, it was found that the test parameters that significantly affect curvature ductility values are not sufficient to explain the displacement ductility values. On the other hand, it was seen that the estimation strength of ANNs proved to be greater than MLR in both curvature ductility and displacement ductility. Outcomes also indicated that the NLR model exhibits superior performance for estimating displacement ductility. | ||
| 650 | 4 | |a earthquake |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a ductility |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a pushover analysis |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a neural networks |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a regression analyses |7 (dpeaa)DE-He213 | |
| 773 | 0 | 8 | |i Enthalten in |t KSCE journal of civil engineering |d Seoul : Korean Soc. of Civil Engineers, 1997 |g 16(2012), 5 vom: 29. Juni, Seite 759-770 |w (DE-627)57517238X |w (DE-600)2446036-9 |x 1976-3808 |7 nnns |
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10.1007/s12205-012-0958-1 doi (DE-627)SPR02525846X (SPR)s12205-012-0958-1-e DE-627 ger DE-627 rakwb eng Arslan, M. Hakan verfasserin aut Estimation of curvature and displacement ductility in reinforced concrete buildings 2012 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Korean Society of Civil Engineers and Springer-Verlag Berlin Heidelberg 2012 Abstract Ensuring sufficient ductility in building load bearing systems and elements of the load bearing system is quite important for their seismic performance. The Seismic Codes stipulate that certain requirements must be met to maintain ductility values above a certain level. The purpose of this study is to determine how ductility values of both elements and load bearing systems vary as parameters related to the conditions specified in the codes change and as estimates of these values are used. With this aim in mind, the curvature ductility in columns and beams of a four-storey Reinforced Concrete (RC) building differs depending on parameters that include the axial load level, longitudinal reinforcement, transverse reinforcement, compression bar ratio and concrete strength. The value of the curvature ductility was found to vary according to the number of parameters and variance range, which was found to be 60 and 135 in the beam section and column section, respectively. Later, a pushover analysis was applied to 540 different statuses of the sample RC system for the same parameters, and the ratio variations and respective displacement (global) ductility of the frames were calculated. The relationship between obtained ductility values with the parameters, as well as the accuracy of the established model, were estimated using regression analyses (Multi-linear and Nonlinear Regression (MLR, NLR)) and 11 various Artificial Neural Networks (ANN) methods. According to the estimation methods, it was found that the test parameters that significantly affect curvature ductility values are not sufficient to explain the displacement ductility values. On the other hand, it was seen that the estimation strength of ANNs proved to be greater than MLR in both curvature ductility and displacement ductility. Outcomes also indicated that the NLR model exhibits superior performance for estimating displacement ductility. earthquake (dpeaa)DE-He213 ductility (dpeaa)DE-He213 pushover analysis (dpeaa)DE-He213 neural networks (dpeaa)DE-He213 regression analyses (dpeaa)DE-He213 Enthalten in KSCE journal of civil engineering Seoul : Korean Soc. of Civil Engineers, 1997 16(2012), 5 vom: 29. Juni, Seite 759-770 (DE-627)57517238X (DE-600)2446036-9 1976-3808 nnns volume:16 year:2012 number:5 day:29 month:06 pages:759-770 https://dx.doi.org/10.1007/s12205-012-0958-1 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 16 2012 5 29 06 759-770 |
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10.1007/s12205-012-0958-1 doi (DE-627)SPR02525846X (SPR)s12205-012-0958-1-e DE-627 ger DE-627 rakwb eng Arslan, M. Hakan verfasserin aut Estimation of curvature and displacement ductility in reinforced concrete buildings 2012 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Korean Society of Civil Engineers and Springer-Verlag Berlin Heidelberg 2012 Abstract Ensuring sufficient ductility in building load bearing systems and elements of the load bearing system is quite important for their seismic performance. The Seismic Codes stipulate that certain requirements must be met to maintain ductility values above a certain level. The purpose of this study is to determine how ductility values of both elements and load bearing systems vary as parameters related to the conditions specified in the codes change and as estimates of these values are used. With this aim in mind, the curvature ductility in columns and beams of a four-storey Reinforced Concrete (RC) building differs depending on parameters that include the axial load level, longitudinal reinforcement, transverse reinforcement, compression bar ratio and concrete strength. The value of the curvature ductility was found to vary according to the number of parameters and variance range, which was found to be 60 and 135 in the beam section and column section, respectively. Later, a pushover analysis was applied to 540 different statuses of the sample RC system for the same parameters, and the ratio variations and respective displacement (global) ductility of the frames were calculated. The relationship between obtained ductility values with the parameters, as well as the accuracy of the established model, were estimated using regression analyses (Multi-linear and Nonlinear Regression (MLR, NLR)) and 11 various Artificial Neural Networks (ANN) methods. According to the estimation methods, it was found that the test parameters that significantly affect curvature ductility values are not sufficient to explain the displacement ductility values. On the other hand, it was seen that the estimation strength of ANNs proved to be greater than MLR in both curvature ductility and displacement ductility. Outcomes also indicated that the NLR model exhibits superior performance for estimating displacement ductility. earthquake (dpeaa)DE-He213 ductility (dpeaa)DE-He213 pushover analysis (dpeaa)DE-He213 neural networks (dpeaa)DE-He213 regression analyses (dpeaa)DE-He213 Enthalten in KSCE journal of civil engineering Seoul : Korean Soc. of Civil Engineers, 1997 16(2012), 5 vom: 29. Juni, Seite 759-770 (DE-627)57517238X (DE-600)2446036-9 1976-3808 nnns volume:16 year:2012 number:5 day:29 month:06 pages:759-770 https://dx.doi.org/10.1007/s12205-012-0958-1 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 16 2012 5 29 06 759-770 |
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10.1007/s12205-012-0958-1 doi (DE-627)SPR02525846X (SPR)s12205-012-0958-1-e DE-627 ger DE-627 rakwb eng Arslan, M. Hakan verfasserin aut Estimation of curvature and displacement ductility in reinforced concrete buildings 2012 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Korean Society of Civil Engineers and Springer-Verlag Berlin Heidelberg 2012 Abstract Ensuring sufficient ductility in building load bearing systems and elements of the load bearing system is quite important for their seismic performance. The Seismic Codes stipulate that certain requirements must be met to maintain ductility values above a certain level. The purpose of this study is to determine how ductility values of both elements and load bearing systems vary as parameters related to the conditions specified in the codes change and as estimates of these values are used. With this aim in mind, the curvature ductility in columns and beams of a four-storey Reinforced Concrete (RC) building differs depending on parameters that include the axial load level, longitudinal reinforcement, transverse reinforcement, compression bar ratio and concrete strength. The value of the curvature ductility was found to vary according to the number of parameters and variance range, which was found to be 60 and 135 in the beam section and column section, respectively. Later, a pushover analysis was applied to 540 different statuses of the sample RC system for the same parameters, and the ratio variations and respective displacement (global) ductility of the frames were calculated. The relationship between obtained ductility values with the parameters, as well as the accuracy of the established model, were estimated using regression analyses (Multi-linear and Nonlinear Regression (MLR, NLR)) and 11 various Artificial Neural Networks (ANN) methods. According to the estimation methods, it was found that the test parameters that significantly affect curvature ductility values are not sufficient to explain the displacement ductility values. On the other hand, it was seen that the estimation strength of ANNs proved to be greater than MLR in both curvature ductility and displacement ductility. Outcomes also indicated that the NLR model exhibits superior performance for estimating displacement ductility. earthquake (dpeaa)DE-He213 ductility (dpeaa)DE-He213 pushover analysis (dpeaa)DE-He213 neural networks (dpeaa)DE-He213 regression analyses (dpeaa)DE-He213 Enthalten in KSCE journal of civil engineering Seoul : Korean Soc. of Civil Engineers, 1997 16(2012), 5 vom: 29. Juni, Seite 759-770 (DE-627)57517238X (DE-600)2446036-9 1976-3808 nnns volume:16 year:2012 number:5 day:29 month:06 pages:759-770 https://dx.doi.org/10.1007/s12205-012-0958-1 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 16 2012 5 29 06 759-770 |
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10.1007/s12205-012-0958-1 doi (DE-627)SPR02525846X (SPR)s12205-012-0958-1-e DE-627 ger DE-627 rakwb eng Arslan, M. Hakan verfasserin aut Estimation of curvature and displacement ductility in reinforced concrete buildings 2012 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Korean Society of Civil Engineers and Springer-Verlag Berlin Heidelberg 2012 Abstract Ensuring sufficient ductility in building load bearing systems and elements of the load bearing system is quite important for their seismic performance. The Seismic Codes stipulate that certain requirements must be met to maintain ductility values above a certain level. The purpose of this study is to determine how ductility values of both elements and load bearing systems vary as parameters related to the conditions specified in the codes change and as estimates of these values are used. With this aim in mind, the curvature ductility in columns and beams of a four-storey Reinforced Concrete (RC) building differs depending on parameters that include the axial load level, longitudinal reinforcement, transverse reinforcement, compression bar ratio and concrete strength. The value of the curvature ductility was found to vary according to the number of parameters and variance range, which was found to be 60 and 135 in the beam section and column section, respectively. Later, a pushover analysis was applied to 540 different statuses of the sample RC system for the same parameters, and the ratio variations and respective displacement (global) ductility of the frames were calculated. The relationship between obtained ductility values with the parameters, as well as the accuracy of the established model, were estimated using regression analyses (Multi-linear and Nonlinear Regression (MLR, NLR)) and 11 various Artificial Neural Networks (ANN) methods. According to the estimation methods, it was found that the test parameters that significantly affect curvature ductility values are not sufficient to explain the displacement ductility values. On the other hand, it was seen that the estimation strength of ANNs proved to be greater than MLR in both curvature ductility and displacement ductility. Outcomes also indicated that the NLR model exhibits superior performance for estimating displacement ductility. earthquake (dpeaa)DE-He213 ductility (dpeaa)DE-He213 pushover analysis (dpeaa)DE-He213 neural networks (dpeaa)DE-He213 regression analyses (dpeaa)DE-He213 Enthalten in KSCE journal of civil engineering Seoul : Korean Soc. of Civil Engineers, 1997 16(2012), 5 vom: 29. Juni, Seite 759-770 (DE-627)57517238X (DE-600)2446036-9 1976-3808 nnns volume:16 year:2012 number:5 day:29 month:06 pages:759-770 https://dx.doi.org/10.1007/s12205-012-0958-1 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 16 2012 5 29 06 759-770 |
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10.1007/s12205-012-0958-1 doi (DE-627)SPR02525846X (SPR)s12205-012-0958-1-e DE-627 ger DE-627 rakwb eng Arslan, M. Hakan verfasserin aut Estimation of curvature and displacement ductility in reinforced concrete buildings 2012 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Korean Society of Civil Engineers and Springer-Verlag Berlin Heidelberg 2012 Abstract Ensuring sufficient ductility in building load bearing systems and elements of the load bearing system is quite important for their seismic performance. The Seismic Codes stipulate that certain requirements must be met to maintain ductility values above a certain level. The purpose of this study is to determine how ductility values of both elements and load bearing systems vary as parameters related to the conditions specified in the codes change and as estimates of these values are used. With this aim in mind, the curvature ductility in columns and beams of a four-storey Reinforced Concrete (RC) building differs depending on parameters that include the axial load level, longitudinal reinforcement, transverse reinforcement, compression bar ratio and concrete strength. The value of the curvature ductility was found to vary according to the number of parameters and variance range, which was found to be 60 and 135 in the beam section and column section, respectively. Later, a pushover analysis was applied to 540 different statuses of the sample RC system for the same parameters, and the ratio variations and respective displacement (global) ductility of the frames were calculated. The relationship between obtained ductility values with the parameters, as well as the accuracy of the established model, were estimated using regression analyses (Multi-linear and Nonlinear Regression (MLR, NLR)) and 11 various Artificial Neural Networks (ANN) methods. According to the estimation methods, it was found that the test parameters that significantly affect curvature ductility values are not sufficient to explain the displacement ductility values. On the other hand, it was seen that the estimation strength of ANNs proved to be greater than MLR in both curvature ductility and displacement ductility. Outcomes also indicated that the NLR model exhibits superior performance for estimating displacement ductility. earthquake (dpeaa)DE-He213 ductility (dpeaa)DE-He213 pushover analysis (dpeaa)DE-He213 neural networks (dpeaa)DE-He213 regression analyses (dpeaa)DE-He213 Enthalten in KSCE journal of civil engineering Seoul : Korean Soc. of Civil Engineers, 1997 16(2012), 5 vom: 29. Juni, Seite 759-770 (DE-627)57517238X (DE-600)2446036-9 1976-3808 nnns volume:16 year:2012 number:5 day:29 month:06 pages:759-770 https://dx.doi.org/10.1007/s12205-012-0958-1 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 16 2012 5 29 06 759-770 |
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The Seismic Codes stipulate that certain requirements must be met to maintain ductility values above a certain level. The purpose of this study is to determine how ductility values of both elements and load bearing systems vary as parameters related to the conditions specified in the codes change and as estimates of these values are used. With this aim in mind, the curvature ductility in columns and beams of a four-storey Reinforced Concrete (RC) building differs depending on parameters that include the axial load level, longitudinal reinforcement, transverse reinforcement, compression bar ratio and concrete strength. The value of the curvature ductility was found to vary according to the number of parameters and variance range, which was found to be 60 and 135 in the beam section and column section, respectively. Later, a pushover analysis was applied to 540 different statuses of the sample RC system for the same parameters, and the ratio variations and respective displacement (global) ductility of the frames were calculated. The relationship between obtained ductility values with the parameters, as well as the accuracy of the established model, were estimated using regression analyses (Multi-linear and Nonlinear Regression (MLR, NLR)) and 11 various Artificial Neural Networks (ANN) methods. According to the estimation methods, it was found that the test parameters that significantly affect curvature ductility values are not sufficient to explain the displacement ductility values. On the other hand, it was seen that the estimation strength of ANNs proved to be greater than MLR in both curvature ductility and displacement ductility. 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| author |
Arslan, M. Hakan |
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Arslan, M. Hakan misc earthquake misc ductility misc pushover analysis misc neural networks misc regression analyses Estimation of curvature and displacement ductility in reinforced concrete buildings |
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| topic_title |
Estimation of curvature and displacement ductility in reinforced concrete buildings earthquake (dpeaa)DE-He213 ductility (dpeaa)DE-He213 pushover analysis (dpeaa)DE-He213 neural networks (dpeaa)DE-He213 regression analyses (dpeaa)DE-He213 |
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misc earthquake misc ductility misc pushover analysis misc neural networks misc regression analyses |
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misc earthquake misc ductility misc pushover analysis misc neural networks misc regression analyses |
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Estimation of curvature and displacement ductility in reinforced concrete buildings |
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Estimation of curvature and displacement ductility in reinforced concrete buildings |
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Arslan, M. Hakan |
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10.1007/s12205-012-0958-1 |
| title_sort |
estimation of curvature and displacement ductility in reinforced concrete buildings |
| title_auth |
Estimation of curvature and displacement ductility in reinforced concrete buildings |
| abstract |
Abstract Ensuring sufficient ductility in building load bearing systems and elements of the load bearing system is quite important for their seismic performance. The Seismic Codes stipulate that certain requirements must be met to maintain ductility values above a certain level. The purpose of this study is to determine how ductility values of both elements and load bearing systems vary as parameters related to the conditions specified in the codes change and as estimates of these values are used. With this aim in mind, the curvature ductility in columns and beams of a four-storey Reinforced Concrete (RC) building differs depending on parameters that include the axial load level, longitudinal reinforcement, transverse reinforcement, compression bar ratio and concrete strength. The value of the curvature ductility was found to vary according to the number of parameters and variance range, which was found to be 60 and 135 in the beam section and column section, respectively. Later, a pushover analysis was applied to 540 different statuses of the sample RC system for the same parameters, and the ratio variations and respective displacement (global) ductility of the frames were calculated. The relationship between obtained ductility values with the parameters, as well as the accuracy of the established model, were estimated using regression analyses (Multi-linear and Nonlinear Regression (MLR, NLR)) and 11 various Artificial Neural Networks (ANN) methods. According to the estimation methods, it was found that the test parameters that significantly affect curvature ductility values are not sufficient to explain the displacement ductility values. On the other hand, it was seen that the estimation strength of ANNs proved to be greater than MLR in both curvature ductility and displacement ductility. Outcomes also indicated that the NLR model exhibits superior performance for estimating displacement ductility. © Korean Society of Civil Engineers and Springer-Verlag Berlin Heidelberg 2012 |
| abstractGer |
Abstract Ensuring sufficient ductility in building load bearing systems and elements of the load bearing system is quite important for their seismic performance. The Seismic Codes stipulate that certain requirements must be met to maintain ductility values above a certain level. The purpose of this study is to determine how ductility values of both elements and load bearing systems vary as parameters related to the conditions specified in the codes change and as estimates of these values are used. With this aim in mind, the curvature ductility in columns and beams of a four-storey Reinforced Concrete (RC) building differs depending on parameters that include the axial load level, longitudinal reinforcement, transverse reinforcement, compression bar ratio and concrete strength. The value of the curvature ductility was found to vary according to the number of parameters and variance range, which was found to be 60 and 135 in the beam section and column section, respectively. Later, a pushover analysis was applied to 540 different statuses of the sample RC system for the same parameters, and the ratio variations and respective displacement (global) ductility of the frames were calculated. The relationship between obtained ductility values with the parameters, as well as the accuracy of the established model, were estimated using regression analyses (Multi-linear and Nonlinear Regression (MLR, NLR)) and 11 various Artificial Neural Networks (ANN) methods. According to the estimation methods, it was found that the test parameters that significantly affect curvature ductility values are not sufficient to explain the displacement ductility values. On the other hand, it was seen that the estimation strength of ANNs proved to be greater than MLR in both curvature ductility and displacement ductility. Outcomes also indicated that the NLR model exhibits superior performance for estimating displacement ductility. © Korean Society of Civil Engineers and Springer-Verlag Berlin Heidelberg 2012 |
| abstract_unstemmed |
Abstract Ensuring sufficient ductility in building load bearing systems and elements of the load bearing system is quite important for their seismic performance. The Seismic Codes stipulate that certain requirements must be met to maintain ductility values above a certain level. The purpose of this study is to determine how ductility values of both elements and load bearing systems vary as parameters related to the conditions specified in the codes change and as estimates of these values are used. With this aim in mind, the curvature ductility in columns and beams of a four-storey Reinforced Concrete (RC) building differs depending on parameters that include the axial load level, longitudinal reinforcement, transverse reinforcement, compression bar ratio and concrete strength. The value of the curvature ductility was found to vary according to the number of parameters and variance range, which was found to be 60 and 135 in the beam section and column section, respectively. Later, a pushover analysis was applied to 540 different statuses of the sample RC system for the same parameters, and the ratio variations and respective displacement (global) ductility of the frames were calculated. The relationship between obtained ductility values with the parameters, as well as the accuracy of the established model, were estimated using regression analyses (Multi-linear and Nonlinear Regression (MLR, NLR)) and 11 various Artificial Neural Networks (ANN) methods. According to the estimation methods, it was found that the test parameters that significantly affect curvature ductility values are not sufficient to explain the displacement ductility values. On the other hand, it was seen that the estimation strength of ANNs proved to be greater than MLR in both curvature ductility and displacement ductility. Outcomes also indicated that the NLR model exhibits superior performance for estimating displacement ductility. © Korean Society of Civil Engineers and Springer-Verlag Berlin Heidelberg 2012 |
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Estimation of curvature and displacement ductility in reinforced concrete buildings |
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https://dx.doi.org/10.1007/s12205-012-0958-1 |
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| score |
7.4015427 |