Dynamic wind response of tall buildings using artificial neural network

Abstract

Summary This paper presents an alternative approach for predicting the dynamic wind response of tall buildings using artificial neural network (ANN). The ANN model was developed, trained, and validated based on the data generated in the context of Indian Wind Code (IWC), IS 875 (Part 3):2015. According to the IWC, dynamic wind responses can be calculated for a specific configuration of buildings. The dynamic wind loads and their corresponding responses of structures other than the specified configurations in IWC have to be estimated by wind tunnel tests or computational techniques, which are expensive and time intensive. Alternatively, ANN is an efficient and economical computational analysis tool that can be implemented to estimate the dynamic wind response of a building. In this paper, ANN models were developed to predict base shear and base bending moment of a tall building in along‐ and across‐wind direction by giving the input as the configuration of the building, wind velocity, and terrain category. Multilayer perceptron ANN models with back‐propagation training algorithm was adopted. On comparison of results, it was found that the predicted values obtained from the ANN models and the calculated responses acquired using IWC standards are almost similar. Using the best fit model of ANN, an extensive parametric study was performed to predict the dynamic wind response of tall buildings for the configurations on which IWC is silent. Based on the results obtained from this study, design charts are developed for the prediction of dynamic wind response of tall buildings.

Application of artificial neural network for predicting dynamic along‐wind response of tall buildings

“…In the first stage, the procedure explained by Nikose and Sonparote [ 32 ] for the development and training of the ANN model is adopted in the present work. In the second stage, prediction of along‐wind response in terms of base shear and bending moment values is generated for the aspect ratios ( h / b ) of 3 to 20 and side ratios ( d / b ) of 0.5 to 3.5 in all the wind zones and terrain categories as per given in IS 875 (Part 3): 2015 for the building heights ranging from 100 to 250 m. In the third stage, these extensive ANN predicted responses of dynamic along‐wind responses are fitted and proposed in the form of simplified empirical equations as the function of the aspect ( h / b ) and side ( d / b ) ratios, wind velocity ( V b ), and terrain category (TC).…”

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

“…The comparison can be aptly carried out using Artificial Neural Network (ANN), which is a proficient tool for analyzing complex engineering problems and also proven to deliver feasible solutions. ANN can be effectively used to develop functional relationships between input and associated output parameters derived from any source [16]. Thus, ANN can be conveniently used to create a generalized relationship from limited and occasionally erratic input data.…”

“…Neural nets were used to estimate mean external surface pressure coefficients of tall buildings [28] and to propose a simplified approach for estimating the dynamic along-wind response of tall buildings based on Indian Wind Code [29,30]. Again for stabilizing proper orthogonal decomposition-based reduced-order models for quasi-static geophysical turbulent flows [31], to propose an efficient and cost-effective computational tool that can be applied to estimate the wind response of a building [16], ANN was used. ANN also helped the researchers to present a numerical methodology combining regression analysis with flow modal decomposition for constructing reduced-order models of fluid flows [32] and also to develop equations of wind-induced pressure coefficient using experimental data, for the group method of data handling neural network that can efficiently predict average surface pressure coefficients on the projected surface of different C-shaped building models [33].…”

Optimization of alongwind and crosswind force coefficients on a tall building with horizontal limbs using surrogate modeling

“…ANN can be effectively used to develop functional relationships between input and associated output parameters derived from any source. [ 17 ] Thus, ANN can be conveniently used to create a generalized relationship from limited and occasionally erratic input data. Many researchers had applied and reported ANN to be extremely efficient to solve complex engineering problems.…”

“…[ 20 ] Neural nets were used to estimate mean external surface pressure coefficients of tall buildings [ 21 ] and to propose a simplified approach for estimating the dynamic alongwind response of tall buildings based on Indian Wind Code. [ 22,23 ] Again, for stabilizing proper orthogonal decomposition‐based reduced‐order models for quasi‐static geophysical turbulent flows, [ 24 ] to propose an efficient and cost‐effective computational tool that can be applied to estimate the wind response of a building, [ 17 ] ANN was used. ANN helped the researchers to present a numerical methodology combining regression analysis with flow modal decomposition for constructing reduced‐order models of fluid flows.…”