A platform for research: civil engineering, architecture and urbanism
A platform for research: civil engineering, architecture and urbanism
Prediction of Wind-Induced Pressure on Pentagon Plan Shape Building using Artificial Neural Network
https://orcid.org/http://orcid.org/0000-0003-2405-3898
https://orcid.org/http://orcid.org/0000-0001-6110-5912
https://orcid.org/http://orcid.org/0000-0002-6839-5497
Nowadays, with the development of composite materials and construction technique, it is feasible to construct tall buildings to a desired height. In the design of tall buildings, wind loads are the major design criteria to be considered by structural engineers and architects. This paper explores the mean pressure coefficient (mean Cp) on pentagon plan shape building using artificial neural networks (ANN). The input for ANN is obtained by performing CFD simulation for the wind angles 0° to 180° at an interval of 15°. The Levenberg–Marquardt training function and mean square error (MSE) performance function are utilized to train the target data. The network is trained till the correlation (R) reaches between 0.9 and 1, respectively. The results have shown that the mean values of Cp obtained from CFD and ANN are in good agreement. Furthermore, mean pressure coefficient (Cp) for intermediate wind angles for pentagon plan shape building is obtained using ANN. A sample check is made on the wind angles 35° and 165°, and the obtained results are within the permissible limits.
Prediction of Wind-Induced Pressure on Pentagon Plan Shape Building using Artificial Neural Network
https://orcid.org/http://orcid.org/0000-0003-2405-3898
https://orcid.org/http://orcid.org/0000-0001-6110-5912
https://orcid.org/http://orcid.org/0000-0002-6839-5497
Nowadays, with the development of composite materials and construction technique, it is feasible to construct tall buildings to a desired height. In the design of tall buildings, wind loads are the major design criteria to be considered by structural engineers and architects. This paper explores the mean pressure coefficient (mean Cp) on pentagon plan shape building using artificial neural networks (ANN). The input for ANN is obtained by performing CFD simulation for the wind angles 0° to 180° at an interval of 15°. The Levenberg–Marquardt training function and mean square error (MSE) performance function are utilized to train the target data. The network is trained till the correlation (R) reaches between 0.9 and 1, respectively. The results have shown that the mean values of Cp obtained from CFD and ANN are in good agreement. Furthermore, mean pressure coefficient (Cp) for intermediate wind angles for pentagon plan shape building is obtained using ANN. A sample check is made on the wind angles 35° and 165°, and the obtained results are within the permissible limits.
Prediction of Wind-Induced Pressure on Pentagon Plan Shape Building using Artificial Neural Network
https://orcid.org/http://orcid.org/0000-0003-2405-3898
https://orcid.org/http://orcid.org/0000-0001-6110-5912
https://orcid.org/http://orcid.org/0000-0002-6839-5497
Nowadays, with the development of composite materials and construction technique, it is feasible to construct tall buildings to a desired height. In the design of tall buildings, wind loads are the major design criteria to be considered by structural engineers and architects. This paper explores the mean pressure coefficient (mean Cp) on pentagon plan shape building using artificial neural networks (ANN). The input for ANN is obtained by performing CFD simulation for the wind angles 0° to 180° at an interval of 15°. The Levenberg–Marquardt training function and mean square error (MSE) performance function are utilized to train the target data. The network is trained till the correlation (R) reaches between 0.9 and 1, respectively. The results have shown that the mean values of Cp obtained from CFD and ANN are in good agreement. Furthermore, mean pressure coefficient (Cp) for intermediate wind angles for pentagon plan shape building is obtained using ANN. A sample check is made on the wind angles 35° and 165°, and the obtained results are within the permissible limits.
Prediction of Wind-Induced Pressure on Pentagon Plan Shape Building using Artificial Neural Network
J. Inst. Eng. India Ser. A
Vigneshwaran, R. (author) / Prabavathy, S. (author) / Sivasubramanian, J. (author)
Journal of The Institution of Engineers (India): Series A ; 103 ; 581-599
2022-06-01
19 pages
Article (Journal)
Electronic Resource
English
Forecasting of Wind Induced Pressure on Setback Building Using Artificial Neural Network
| BASE | 2020
Artificial Neural Network-Based Prediction of Wind Pressure Coefficients on Buildings
| Springer Verlag | 2021
The Pentagon Building Performance Study
| Online Contents | 2005
Study on Outdoor Wind Flow and Mean Wind Pressure around the ‘T’ Plan Shape Tall Building
| Springer Verlag | 2022
Interpolation of wind-induced pressure time series with an artificial neural network
| Tema Archive | 2002