Forecasting Aerodynamic Coefficients of Bi-Axial Symmetric C Plan-Shaped Tall Buildings Using ANFIS

Abstract This study addressed the challenge of forecasting the critical Angle of Attack (AOA) for tall buildings, which can significantly affect wind forces. Extensive assessments are needed to optimise the wind force on a tall building, either by wind tunnel testing or computational fluid dynamics...
Ausführliche Beschreibung

Gespeichert in:

Autor*in:	Verma, Himanshoo [verfasserIn] Sonparote, Ranjan S. [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2024

Schlagwörter:	Tall building Adaptive neuro-fuzzy inference system (ANFIS) Wind analysis Computational fluid dynamics (CFD) Aerodynamic coefficient

Anmerkung:	© Korean Society of Civil Engineers 2024

Übergeordnetes Werk:	Enthalten in: KSCE journal of civil engineering - Korean Society of Civil Engineers, 1997, 28(2024), 6 vom: 01. Apr., Seite 2286-2303
Übergeordnetes Werk:	volume:28 ; year:2024 ; number:6 ; day:01 ; month:04 ; pages:2286-2303

Links:	Volltext

DOI / URN:	10.1007/s12205-024-0982-y

Katalog-ID:	SPR05588038X

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245	1	0	\|a Forecasting Aerodynamic Coefficients of Bi-Axial Symmetric C Plan-Shaped Tall Buildings Using ANFIS
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520			\|a Abstract This study addressed the challenge of forecasting the critical Angle of Attack (AOA) for tall buildings, which can significantly affect wind forces. Extensive assessments are needed to optimise the wind force on a tall building, either by wind tunnel testing or computational fluid dynamics (CFD), using various combinations of AOA and corner cut (CC). This process is time-consuming and complex. This study aims to develop an Adaptive Neuro-Fuzzy Interface System (ANFIS) approach for rapidly and effectively obtaining aerodynamic coefficients. Hence, two symmetrical C-shaped tall buildings, subjected to AOAs ranging from 0° to 90° and different CC modifications, are numerically simulated using CFD, and the outcomes (force coefficient (CF) and moment coefficient (CM)) are used to train and test ANFIS model. The validation showed that the maximum error is less than 4%, indicating its excellent predictability. Furthermore, it is observed that for models A1 (CF = 0.66 and CM = 0.76) and A2 (CF = 0.68 and CM = 0.75), the minimum CF and CM at 90° AOA with a 30% CC are much lower, i.e., 45.4% and 38.7%, 43.3% and 41.4%, respectively than the maximum values at 0° AOA with 0% CC. This ANFIS model can predict aerodynamic coefficients for various combinations of CC and AOAs.
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700	1		\|a Sonparote, Ranjan S. \|e verfasserin \|0 (orcid)0000-0001-5097-3212 \|4 aut
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allfields	10.1007/s12205-024-0982-y doi (DE-627)SPR05588038X (SPR)s12205-024-0982-y-e DE-627 ger DE-627 rakwb eng 620 VZ Verma, Himanshoo verfasserin (orcid)0000-0003-4304-4823 aut Forecasting Aerodynamic Coefficients of Bi-Axial Symmetric C Plan-Shaped Tall Buildings Using ANFIS 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Korean Society of Civil Engineers 2024 Abstract This study addressed the challenge of forecasting the critical Angle of Attack (AOA) for tall buildings, which can significantly affect wind forces. Extensive assessments are needed to optimise the wind force on a tall building, either by wind tunnel testing or computational fluid dynamics (CFD), using various combinations of AOA and corner cut (CC). This process is time-consuming and complex. This study aims to develop an Adaptive Neuro-Fuzzy Interface System (ANFIS) approach for rapidly and effectively obtaining aerodynamic coefficients. Hence, two symmetrical C-shaped tall buildings, subjected to AOAs ranging from 0° to 90° and different CC modifications, are numerically simulated using CFD, and the outcomes (force coefficient (CF) and moment coefficient (CM)) are used to train and test ANFIS model. The validation showed that the maximum error is less than 4%, indicating its excellent predictability. Furthermore, it is observed that for models A1 (CF = 0.66 and CM = 0.76) and A2 (CF = 0.68 and CM = 0.75), the minimum CF and CM at 90° AOA with a 30% CC are much lower, i.e., 45.4% and 38.7%, 43.3% and 41.4%, respectively than the maximum values at 0° AOA with 0% CC. This ANFIS model can predict aerodynamic coefficients for various combinations of CC and AOAs. Tall building (dpeaa)DE-He213 Adaptive neuro-fuzzy inference system (ANFIS) (dpeaa)DE-He213 Wind analysis (dpeaa)DE-He213 Computational fluid dynamics (CFD) (dpeaa)DE-He213 Aerodynamic coefficient (dpeaa)DE-He213 Sonparote, Ranjan S. verfasserin (orcid)0000-0001-5097-3212 aut Enthalten in KSCE journal of civil engineering Korean Society of Civil Engineers, 1997 28(2024), 6 vom: 01. Apr., Seite 2286-2303 (DE-627)57517238X (DE-600)2446036-9 1976-3808 nnns volume:28 year:2024 number:6 day:01 month:04 pages:2286-2303 https://dx.doi.org/10.1007/s12205-024-0982-y X:SPRINGER Resolving-System lizenzpflichtig Volltext SYSFLAG_0 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_152 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_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_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 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_2118 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_4126 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_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 28 2024 6 01 04 2286-2303
spelling	10.1007/s12205-024-0982-y doi (DE-627)SPR05588038X (SPR)s12205-024-0982-y-e DE-627 ger DE-627 rakwb eng 620 VZ Verma, Himanshoo verfasserin (orcid)0000-0003-4304-4823 aut Forecasting Aerodynamic Coefficients of Bi-Axial Symmetric C Plan-Shaped Tall Buildings Using ANFIS 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Korean Society of Civil Engineers 2024 Abstract This study addressed the challenge of forecasting the critical Angle of Attack (AOA) for tall buildings, which can significantly affect wind forces. Extensive assessments are needed to optimise the wind force on a tall building, either by wind tunnel testing or computational fluid dynamics (CFD), using various combinations of AOA and corner cut (CC). This process is time-consuming and complex. This study aims to develop an Adaptive Neuro-Fuzzy Interface System (ANFIS) approach for rapidly and effectively obtaining aerodynamic coefficients. Hence, two symmetrical C-shaped tall buildings, subjected to AOAs ranging from 0° to 90° and different CC modifications, are numerically simulated using CFD, and the outcomes (force coefficient (CF) and moment coefficient (CM)) are used to train and test ANFIS model. The validation showed that the maximum error is less than 4%, indicating its excellent predictability. Furthermore, it is observed that for models A1 (CF = 0.66 and CM = 0.76) and A2 (CF = 0.68 and CM = 0.75), the minimum CF and CM at 90° AOA with a 30% CC are much lower, i.e., 45.4% and 38.7%, 43.3% and 41.4%, respectively than the maximum values at 0° AOA with 0% CC. This ANFIS model can predict aerodynamic coefficients for various combinations of CC and AOAs. Tall building (dpeaa)DE-He213 Adaptive neuro-fuzzy inference system (ANFIS) (dpeaa)DE-He213 Wind analysis (dpeaa)DE-He213 Computational fluid dynamics (CFD) (dpeaa)DE-He213 Aerodynamic coefficient (dpeaa)DE-He213 Sonparote, Ranjan S. verfasserin (orcid)0000-0001-5097-3212 aut Enthalten in KSCE journal of civil engineering Korean Society of Civil Engineers, 1997 28(2024), 6 vom: 01. Apr., Seite 2286-2303 (DE-627)57517238X (DE-600)2446036-9 1976-3808 nnns volume:28 year:2024 number:6 day:01 month:04 pages:2286-2303 https://dx.doi.org/10.1007/s12205-024-0982-y X:SPRINGER Resolving-System lizenzpflichtig Volltext SYSFLAG_0 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_152 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_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_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 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_2118 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_4126 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_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 28 2024 6 01 04 2286-2303
allfields_unstemmed	10.1007/s12205-024-0982-y doi (DE-627)SPR05588038X (SPR)s12205-024-0982-y-e DE-627 ger DE-627 rakwb eng 620 VZ Verma, Himanshoo verfasserin (orcid)0000-0003-4304-4823 aut Forecasting Aerodynamic Coefficients of Bi-Axial Symmetric C Plan-Shaped Tall Buildings Using ANFIS 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Korean Society of Civil Engineers 2024 Abstract This study addressed the challenge of forecasting the critical Angle of Attack (AOA) for tall buildings, which can significantly affect wind forces. Extensive assessments are needed to optimise the wind force on a tall building, either by wind tunnel testing or computational fluid dynamics (CFD), using various combinations of AOA and corner cut (CC). This process is time-consuming and complex. This study aims to develop an Adaptive Neuro-Fuzzy Interface System (ANFIS) approach for rapidly and effectively obtaining aerodynamic coefficients. Hence, two symmetrical C-shaped tall buildings, subjected to AOAs ranging from 0° to 90° and different CC modifications, are numerically simulated using CFD, and the outcomes (force coefficient (CF) and moment coefficient (CM)) are used to train and test ANFIS model. The validation showed that the maximum error is less than 4%, indicating its excellent predictability. Furthermore, it is observed that for models A1 (CF = 0.66 and CM = 0.76) and A2 (CF = 0.68 and CM = 0.75), the minimum CF and CM at 90° AOA with a 30% CC are much lower, i.e., 45.4% and 38.7%, 43.3% and 41.4%, respectively than the maximum values at 0° AOA with 0% CC. This ANFIS model can predict aerodynamic coefficients for various combinations of CC and AOAs. Tall building (dpeaa)DE-He213 Adaptive neuro-fuzzy inference system (ANFIS) (dpeaa)DE-He213 Wind analysis (dpeaa)DE-He213 Computational fluid dynamics (CFD) (dpeaa)DE-He213 Aerodynamic coefficient (dpeaa)DE-He213 Sonparote, Ranjan S. verfasserin (orcid)0000-0001-5097-3212 aut Enthalten in KSCE journal of civil engineering Korean Society of Civil Engineers, 1997 28(2024), 6 vom: 01. Apr., Seite 2286-2303 (DE-627)57517238X (DE-600)2446036-9 1976-3808 nnns volume:28 year:2024 number:6 day:01 month:04 pages:2286-2303 https://dx.doi.org/10.1007/s12205-024-0982-y X:SPRINGER Resolving-System lizenzpflichtig Volltext SYSFLAG_0 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_152 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_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_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 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_2118 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_4126 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_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 28 2024 6 01 04 2286-2303
allfieldsGer	10.1007/s12205-024-0982-y doi (DE-627)SPR05588038X (SPR)s12205-024-0982-y-e DE-627 ger DE-627 rakwb eng 620 VZ Verma, Himanshoo verfasserin (orcid)0000-0003-4304-4823 aut Forecasting Aerodynamic Coefficients of Bi-Axial Symmetric C Plan-Shaped Tall Buildings Using ANFIS 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Korean Society of Civil Engineers 2024 Abstract This study addressed the challenge of forecasting the critical Angle of Attack (AOA) for tall buildings, which can significantly affect wind forces. Extensive assessments are needed to optimise the wind force on a tall building, either by wind tunnel testing or computational fluid dynamics (CFD), using various combinations of AOA and corner cut (CC). This process is time-consuming and complex. This study aims to develop an Adaptive Neuro-Fuzzy Interface System (ANFIS) approach for rapidly and effectively obtaining aerodynamic coefficients. Hence, two symmetrical C-shaped tall buildings, subjected to AOAs ranging from 0° to 90° and different CC modifications, are numerically simulated using CFD, and the outcomes (force coefficient (CF) and moment coefficient (CM)) are used to train and test ANFIS model. The validation showed that the maximum error is less than 4%, indicating its excellent predictability. Furthermore, it is observed that for models A1 (CF = 0.66 and CM = 0.76) and A2 (CF = 0.68 and CM = 0.75), the minimum CF and CM at 90° AOA with a 30% CC are much lower, i.e., 45.4% and 38.7%, 43.3% and 41.4%, respectively than the maximum values at 0° AOA with 0% CC. This ANFIS model can predict aerodynamic coefficients for various combinations of CC and AOAs. Tall building (dpeaa)DE-He213 Adaptive neuro-fuzzy inference system (ANFIS) (dpeaa)DE-He213 Wind analysis (dpeaa)DE-He213 Computational fluid dynamics (CFD) (dpeaa)DE-He213 Aerodynamic coefficient (dpeaa)DE-He213 Sonparote, Ranjan S. verfasserin (orcid)0000-0001-5097-3212 aut Enthalten in KSCE journal of civil engineering Korean Society of Civil Engineers, 1997 28(2024), 6 vom: 01. Apr., Seite 2286-2303 (DE-627)57517238X (DE-600)2446036-9 1976-3808 nnns volume:28 year:2024 number:6 day:01 month:04 pages:2286-2303 https://dx.doi.org/10.1007/s12205-024-0982-y X:SPRINGER Resolving-System lizenzpflichtig Volltext SYSFLAG_0 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_152 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_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_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 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_2118 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_4126 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_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 28 2024 6 01 04 2286-2303
allfieldsSound	10.1007/s12205-024-0982-y doi (DE-627)SPR05588038X (SPR)s12205-024-0982-y-e DE-627 ger DE-627 rakwb eng 620 VZ Verma, Himanshoo verfasserin (orcid)0000-0003-4304-4823 aut Forecasting Aerodynamic Coefficients of Bi-Axial Symmetric C Plan-Shaped Tall Buildings Using ANFIS 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Korean Society of Civil Engineers 2024 Abstract This study addressed the challenge of forecasting the critical Angle of Attack (AOA) for tall buildings, which can significantly affect wind forces. Extensive assessments are needed to optimise the wind force on a tall building, either by wind tunnel testing or computational fluid dynamics (CFD), using various combinations of AOA and corner cut (CC). This process is time-consuming and complex. This study aims to develop an Adaptive Neuro-Fuzzy Interface System (ANFIS) approach for rapidly and effectively obtaining aerodynamic coefficients. Hence, two symmetrical C-shaped tall buildings, subjected to AOAs ranging from 0° to 90° and different CC modifications, are numerically simulated using CFD, and the outcomes (force coefficient (CF) and moment coefficient (CM)) are used to train and test ANFIS model. The validation showed that the maximum error is less than 4%, indicating its excellent predictability. Furthermore, it is observed that for models A1 (CF = 0.66 and CM = 0.76) and A2 (CF = 0.68 and CM = 0.75), the minimum CF and CM at 90° AOA with a 30% CC are much lower, i.e., 45.4% and 38.7%, 43.3% and 41.4%, respectively than the maximum values at 0° AOA with 0% CC. This ANFIS model can predict aerodynamic coefficients for various combinations of CC and AOAs. Tall building (dpeaa)DE-He213 Adaptive neuro-fuzzy inference system (ANFIS) (dpeaa)DE-He213 Wind analysis (dpeaa)DE-He213 Computational fluid dynamics (CFD) (dpeaa)DE-He213 Aerodynamic coefficient (dpeaa)DE-He213 Sonparote, Ranjan S. verfasserin (orcid)0000-0001-5097-3212 aut Enthalten in KSCE journal of civil engineering Korean Society of Civil Engineers, 1997 28(2024), 6 vom: 01. Apr., Seite 2286-2303 (DE-627)57517238X (DE-600)2446036-9 1976-3808 nnns volume:28 year:2024 number:6 day:01 month:04 pages:2286-2303 https://dx.doi.org/10.1007/s12205-024-0982-y X:SPRINGER Resolving-System lizenzpflichtig Volltext SYSFLAG_0 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_152 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_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_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 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_2118 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_4126 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_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 28 2024 6 01 04 2286-2303
language	English
source	Enthalten in KSCE journal of civil engineering 28(2024), 6 vom: 01. Apr., Seite 2286-2303 volume:28 year:2024 number:6 day:01 month:04 pages:2286-2303
sourceStr	Enthalten in KSCE journal of civil engineering 28(2024), 6 vom: 01. Apr., Seite 2286-2303 volume:28 year:2024 number:6 day:01 month:04 pages:2286-2303
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	Tall building Adaptive neuro-fuzzy inference system (ANFIS) Wind analysis Computational fluid dynamics (CFD) Aerodynamic coefficient
dewey-raw	620
isfreeaccess_bool	false
container_title	KSCE journal of civil engineering
authorswithroles_txt_mv	Verma, Himanshoo @@aut@@ Sonparote, Ranjan S. @@aut@@
publishDateDaySort_date	2024-04-01T00:00:00Z
hierarchy_top_id	57517238X
dewey-sort	3620
id	SPR05588038X
language_de	englisch
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author	Verma, Himanshoo
spellingShingle	Verma, Himanshoo ddc 620 misc Tall building misc Adaptive neuro-fuzzy inference system (ANFIS) misc Wind analysis misc Computational fluid dynamics (CFD) misc Aerodynamic coefficient Forecasting Aerodynamic Coefficients of Bi-Axial Symmetric C Plan-Shaped Tall Buildings Using ANFIS
authorStr	Verma, Himanshoo
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topic_title	620 VZ Forecasting Aerodynamic Coefficients of Bi-Axial Symmetric C Plan-Shaped Tall Buildings Using ANFIS Tall building (dpeaa)DE-He213 Adaptive neuro-fuzzy inference system (ANFIS) (dpeaa)DE-He213 Wind analysis (dpeaa)DE-He213 Computational fluid dynamics (CFD) (dpeaa)DE-He213 Aerodynamic coefficient (dpeaa)DE-He213
topic	ddc 620 misc Tall building misc Adaptive neuro-fuzzy inference system (ANFIS) misc Wind analysis misc Computational fluid dynamics (CFD) misc Aerodynamic coefficient
topic_unstemmed	ddc 620 misc Tall building misc Adaptive neuro-fuzzy inference system (ANFIS) misc Wind analysis misc Computational fluid dynamics (CFD) misc Aerodynamic coefficient
topic_browse	ddc 620 misc Tall building misc Adaptive neuro-fuzzy inference system (ANFIS) misc Wind analysis misc Computational fluid dynamics (CFD) misc Aerodynamic coefficient
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title	Forecasting Aerodynamic Coefficients of Bi-Axial Symmetric C Plan-Shaped Tall Buildings Using ANFIS
ctrlnum	(DE-627)SPR05588038X (SPR)s12205-024-0982-y-e
title_full	Forecasting Aerodynamic Coefficients of Bi-Axial Symmetric C Plan-Shaped Tall Buildings Using ANFIS
author_sort	Verma, Himanshoo
journal	KSCE journal of civil engineering
journalStr	KSCE journal of civil engineering
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author_browse	Verma, Himanshoo Sonparote, Ranjan S.
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author-letter	Verma, Himanshoo
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title_sort	forecasting aerodynamic coefficients of bi-axial symmetric c plan-shaped tall buildings using anfis
title_auth	Forecasting Aerodynamic Coefficients of Bi-Axial Symmetric C Plan-Shaped Tall Buildings Using ANFIS
abstract	Abstract This study addressed the challenge of forecasting the critical Angle of Attack (AOA) for tall buildings, which can significantly affect wind forces. Extensive assessments are needed to optimise the wind force on a tall building, either by wind tunnel testing or computational fluid dynamics (CFD), using various combinations of AOA and corner cut (CC). This process is time-consuming and complex. This study aims to develop an Adaptive Neuro-Fuzzy Interface System (ANFIS) approach for rapidly and effectively obtaining aerodynamic coefficients. Hence, two symmetrical C-shaped tall buildings, subjected to AOAs ranging from 0° to 90° and different CC modifications, are numerically simulated using CFD, and the outcomes (force coefficient (CF) and moment coefficient (CM)) are used to train and test ANFIS model. The validation showed that the maximum error is less than 4%, indicating its excellent predictability. Furthermore, it is observed that for models A1 (CF = 0.66 and CM = 0.76) and A2 (CF = 0.68 and CM = 0.75), the minimum CF and CM at 90° AOA with a 30% CC are much lower, i.e., 45.4% and 38.7%, 43.3% and 41.4%, respectively than the maximum values at 0° AOA with 0% CC. This ANFIS model can predict aerodynamic coefficients for various combinations of CC and AOAs. © Korean Society of Civil Engineers 2024
abstractGer	Abstract This study addressed the challenge of forecasting the critical Angle of Attack (AOA) for tall buildings, which can significantly affect wind forces. Extensive assessments are needed to optimise the wind force on a tall building, either by wind tunnel testing or computational fluid dynamics (CFD), using various combinations of AOA and corner cut (CC). This process is time-consuming and complex. This study aims to develop an Adaptive Neuro-Fuzzy Interface System (ANFIS) approach for rapidly and effectively obtaining aerodynamic coefficients. Hence, two symmetrical C-shaped tall buildings, subjected to AOAs ranging from 0° to 90° and different CC modifications, are numerically simulated using CFD, and the outcomes (force coefficient (CF) and moment coefficient (CM)) are used to train and test ANFIS model. The validation showed that the maximum error is less than 4%, indicating its excellent predictability. Furthermore, it is observed that for models A1 (CF = 0.66 and CM = 0.76) and A2 (CF = 0.68 and CM = 0.75), the minimum CF and CM at 90° AOA with a 30% CC are much lower, i.e., 45.4% and 38.7%, 43.3% and 41.4%, respectively than the maximum values at 0° AOA with 0% CC. This ANFIS model can predict aerodynamic coefficients for various combinations of CC and AOAs. © Korean Society of Civil Engineers 2024
abstract_unstemmed	Abstract This study addressed the challenge of forecasting the critical Angle of Attack (AOA) for tall buildings, which can significantly affect wind forces. Extensive assessments are needed to optimise the wind force on a tall building, either by wind tunnel testing or computational fluid dynamics (CFD), using various combinations of AOA and corner cut (CC). This process is time-consuming and complex. This study aims to develop an Adaptive Neuro-Fuzzy Interface System (ANFIS) approach for rapidly and effectively obtaining aerodynamic coefficients. Hence, two symmetrical C-shaped tall buildings, subjected to AOAs ranging from 0° to 90° and different CC modifications, are numerically simulated using CFD, and the outcomes (force coefficient (CF) and moment coefficient (CM)) are used to train and test ANFIS model. The validation showed that the maximum error is less than 4%, indicating its excellent predictability. Furthermore, it is observed that for models A1 (CF = 0.66 and CM = 0.76) and A2 (CF = 0.68 and CM = 0.75), the minimum CF and CM at 90° AOA with a 30% CC are much lower, i.e., 45.4% and 38.7%, 43.3% and 41.4%, respectively than the maximum values at 0° AOA with 0% CC. This ANFIS model can predict aerodynamic coefficients for various combinations of CC and AOAs. © Korean Society of Civil Engineers 2024
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title_short	Forecasting Aerodynamic Coefficients of Bi-Axial Symmetric C Plan-Shaped Tall Buildings Using ANFIS
url	https://dx.doi.org/10.1007/s12205-024-0982-y
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author2	Sonparote, Ranjan S.
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doi_str	10.1007/s12205-024-0982-y
up_date	2024-07-03T18:39:40.557Z
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Extensive assessments are needed to optimise the wind force on a tall building, either by wind tunnel testing or computational fluid dynamics (CFD), using various combinations of AOA and corner cut (CC). This process is time-consuming and complex. This study aims to develop an Adaptive Neuro-Fuzzy Interface System (ANFIS) approach for rapidly and effectively obtaining aerodynamic coefficients. Hence, two symmetrical C-shaped tall buildings, subjected to AOAs ranging from 0° to 90° and different CC modifications, are numerically simulated using CFD, and the outcomes (force coefficient (CF) and moment coefficient (CM)) are used to train and test ANFIS model. The validation showed that the maximum error is less than 4%, indicating its excellent predictability. Furthermore, it is observed that for models A1 (CF = 0.66 and CM = 0.76) and A2 (CF = 0.68 and CM = 0.75), the minimum CF and CM at 90° AOA with a 30% CC are much lower, i.e., 45.4% and 38.7%, 43.3% and 41.4%, respectively than the maximum values at 0° AOA with 0% CC. 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Forecasting Aerodynamic Coefficients of Bi-Axial Symmetric C Plan-Shaped Tall Buildings Using ANFIS

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