Prognosis of Wind-tempted Mean Pressure Coefficients of Cross-shaped Tall Buildings Using Artificial Neural Network

The present paper focuses on the study of wind-induced responses of cross-plan shaped tall buildings. Initially, three parametric building models are studied for the purpose with a constant plan area 22500 mm2. The length and velocity scales are taken as 1:300 and 1:5, respectively. Wind angle of attack (WAA) is considered from 0° to 330° with an increment of 30°. At first, the external surface pressure coefficients (Cp) at different faces of the models are carried out for different wind occurrence angles employing Computational Fluid Dynamics method of simulated wind flow. Again, Fast Fourier Transform (FFT) fitted expressions as the sine and cosine function of WAA are proposed for attaining mean wind pressure coefficient on the building faces. The accuracy of the Fourier series expansions is justified by presenting histograms of sum square error (SSE), R2 value and root mean square error (RMSE). The results are also compared by training Artificial Neural Networks (ANN). Training is continued till Regression (R) values are more than 0.99 and Mean Squared Error (MSE) tends to 0, ensuring a close relationship among the outputs and targets. The face-wise value of (Cp) obtained using all three methods, are plotted. The error histograms of the ANN models show that the fitting data errors are spread within a reasonably good range. It is observed that the deviation in the result is not more than 5% in any case. Finally, the ANN predictions are presented for nine parametric models to cover a wide range of possible cross-shaped buildings

Experimental Investigation of Surface Pressure on ‘+’ Plan Shape Tall Building

The variation in pressure distribution with change in wind orientation angle on different faces of a ‘+’ plan shape tall building is studied in this paper. Experiments have been carried out with a rigid model in a boundary layer wind tunnel for wind incidence angles of 0° and 45°. Peculiar pressure distributions on certain faces are observed. Moreover, drastic change in pressure distribution is observed for the two wind angles. Finally, the flow pattern around the model is computed using Computational Fluid Dynamics (CFD) package ANSYS CFX in order to explain the variation in pressure on different faces

Forecasting of Wind Induced Pressure on Setback Building Using Artificial Neural Network

The wind load on an irregular plan shape tall building is quite different compared to a conventional plan shape tall building. Especially the aerodynamic parameters have extreme change due to the variety of setbacks at one or more the disparity of level. This paper highlights the prediction of pressure coefficient on square, single (20 %) setback and double (10 %) setback buildings for any wind incidence angle by CFD simulation and validated with Artificial Neural Network (ANN) and fast Fourier transform. The ANN is a widely used and efficient tool for different types of analyses. The 0° to 180° wind incidence angles (WIAs) considered as input data and respective face wise pressure coefficient (Cp) used as target data. The Levenberg-Marquardt training function and Mean Square Error (MSE) performance function used to train the target data. The face wise graphs of CFD, ANN and FFT are plotted in a single graph and the Cp of the surface checked by any random WIAs. Amazingly, the Cp of random WIA by ANN is almost similar to CFD. Furthermore, the error of ANN is 0.6 % to 2.5 %, which is negligible. According to this predicted graph, the design Cp of any WIA can be easily calculated and implement directly in the design