A platform for research: civil engineering, architecture and urbanism
A platform for research: civil engineering, architecture and urbanism
Forecasting of average surface pressure coefficient of polygonal building models
The consideration of wind loads in the development of lateral load-resisting systems for tall buildings is of utmost importance, given their vulnerability to such loads. Hence, in the design of tall buildings, a meticulous evaluation of wind loads is imperative. To tackle this issue, aerodynamic modifications can be deployed as effective techniques to mitigate wind loads. Building shape and size are also significant parameters that influence wind loads on tall buildings, which can be experimentally measured in wind tunnels and computationally analyzed using Computational Fluid Dynamics (CFD). In this study, the average surface pressure coefficient is computed for various polygonal building models under different Angle of Attack (AOA) conditions. The average surface pressure coefficient varies among the different faces of the building models. One variable is the position of the building surfaces from the centreline of the frontal face, expressed as a percentage, while the other variables are the side number of the polygon (N) and AOA. The average surface pressure coefficient is measured for different scenarios and positions, and the results are utilized to train an Artificial Neural Network (ANN). The ANN training demonstrates a commendable conformity between the predicted models and the input data.
Forecasting of average surface pressure coefficient of polygonal building models
The consideration of wind loads in the development of lateral load-resisting systems for tall buildings is of utmost importance, given their vulnerability to such loads. Hence, in the design of tall buildings, a meticulous evaluation of wind loads is imperative. To tackle this issue, aerodynamic modifications can be deployed as effective techniques to mitigate wind loads. Building shape and size are also significant parameters that influence wind loads on tall buildings, which can be experimentally measured in wind tunnels and computationally analyzed using Computational Fluid Dynamics (CFD). In this study, the average surface pressure coefficient is computed for various polygonal building models under different Angle of Attack (AOA) conditions. The average surface pressure coefficient varies among the different faces of the building models. One variable is the position of the building surfaces from the centreline of the frontal face, expressed as a percentage, while the other variables are the side number of the polygon (N) and AOA. The average surface pressure coefficient is measured for different scenarios and positions, and the results are utilized to train an Artificial Neural Network (ANN). The ANN training demonstrates a commendable conformity between the predicted models and the input data.
Forecasting of average surface pressure coefficient of polygonal building models
Asian J Civ Eng
Meena, Rahul Kumar (author) / Sanyal, Prasenjit (author) / Paswan, Abhishek Prakash (author) / Raj, Ritu (author)
Asian Journal of Civil Engineering ; 24 ; 3907-3918
2023-12-01
12 pages
Article (Journal)
Electronic Resource
English
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