Abstract
This paper presents the development of artificial neural network (ANN) model for predicting the dynamic across-wind response of tall building as per the provisions given in Indian Wind Code (IWC), IS 875 (Part-3):2015. IWC has given the procedure for estimating across-wind load response for limited aspect ratios of the building. For calculating the across-wind response not conforming to the ratios specified in the code, additional time-consuming and expensive studies are required. In order to minimise such tedious studies, an efficient computational technique is essential to determine wind response. ANN proves to be a promising alternative technique for analysing complex engineering problems. Thus, in this paper, using the best-fit model of ANN, predictions are made for the building configurations that are not included in IWC. The results are plotted as a series of charts in terms of base shear and the base bending moment. It is found that the predictions made using the trained and developed ANN models are in good agreement with the desired output.
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Abbreviations
- \( M_{C} \) :
-
Across-wind design peak base bending moment
- z :
-
Any height of building/structure in m
- \( g_{h} \) :
-
Peak factor
- \( f_{\text{c}} \) :
-
The first-mode natural frequency of the building/structure in the crosswind direction in Hz
- \( \bar{V}_{\text{b}} \) :
-
Basic wind speed
- \( \bar{k}_{2,i} \) :
-
Hourly mean wind speed factor for terrain category
- \( k_{4} \) :
-
Importance factor for the cyclonic region
- \( I_{h,i} \) :
-
Turbulence intensity at height h in terrain category i
- \( C_{\text{fs}} \) :
-
Across-wind force spectrum coefficient
- h :
-
The height of building/structure in m
- b :
-
The breadth of the structure normal to the wind, in m
- \( \overline{{p_{h} }} \) :
-
Design hourly mean wind speed pressure corresponding to \( \overline{{V_{h,d} }} \)
- \( \bar{V}_{h,d} \) :
-
Design hourly mean wind speed at height h in m/s
- \( k_{1} \) :
-
Probability factor (risk coefficient)
- \( k_{3} \) :
-
Topography factor
- \( Z_{0,i} \) :
-
Equivalent aerodynamic roughness height
- k :
-
A mode shape power exponent
- β :
-
Damping coefficient of building/structure
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Nikose, T.J., Sonparote, R.S. Computing dynamic across-wind response of tall buildings using artificial neural network. J Supercomput 76, 3788–3813 (2020). https://doi.org/10.1007/s11227-018-2708-8
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DOI: https://doi.org/10.1007/s11227-018-2708-8