Dynamic along wind response of tall buildings using Artificial Neural Network

Nikose, Trupti; Sonparote, Ranjan S.

doi:10.1007/s10586-018-2027-0

Cited by 11 publications

(8 citation statements)

References 12 publications

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“…Several combinations tried to achieve the best fitted ANN model which can provide an appropriate solution with minimum error. A training procedure adopted by Nikose and Sonparote [29] has been applied in the present study. Initially, maximum epochs were set to 200, but it is found that this number of epochs are not sufficient for the considered problem.…”

Section: Mlp Neural Network Architecturementioning

confidence: 99%

“…Elshaer et al [28] utilized ANN in building corner optimization technique to reduce the wind load effect on tall buildings. [29] demonstrated an application of ANN to determine the dynamic along-wind response as per Indian Wind Code (IWC) and proposed graphs that can predict the dynamic along-wind responses. The present study is a continuation of the previous work done by the authors.…”

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confidence: 99%

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Dynamic wind response of tall buildings using artificial neural network

Nikose

Sonparote

2019

Structural Design Tall Build

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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.

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Section: Mlp Neural Network Architecturementioning

confidence: 99%

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confidence: 99%

Dynamic wind response of tall buildings using artificial neural network

Nikose

Sonparote

2019

Structural Design Tall Build

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“…Nikose and Sonparote [ 29,30 ] performed the parametric study to determine the best trained ANN model that can predict the dynamic along‐wind response and across‐wind response, respectively, based on the Indian Wind Standard's provisions for tall buildings. From the study carried out by authors, it is proved that ANN can be a reliable computational technique that can be used for the prediction of dynamic wind response of tall buildings.…”

Section: Introductionmentioning

confidence: 99%

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

Nikose

Sonparote

2021

Structural Design Tall Build

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Summary This paper presents the simplified approach based on the artificial neural network (ANN) for predictions of the dynamic along‐wind response in the context of the Indian Wind Standard IS 875 (Part 3): 2015. This standard provides direct guidelines for the specific aspect ratio of buildings (h/b ratio as 3, 5, 10, and 20) for assessing along‐wind loads. Evaluation of dynamic wind load and its responses, for the aspect ratio not specified in the IS 875 (Part 3): 2015, may have to be estimated by the boundary layer wind tunnel experiment or other computational techniques. Both the experimentation and computational methods are expensive in terms of time and the resources required. Alternatively, ANN is an efficient and cost‐effective computational technique that can be employed to estimate the dynamic wind response of a building. In this paper, ANN predicted estimations are presented for calculating dynamic along‐wind base shear and base bending moment of a tall building. The response is generated using the best fit ANN model for the aspect ratio (h/b) ranging in between 3 and 20. Finally, these responses were used to propose a simplified empirical equation as a function of aspect ratio, side ratio, wind velocity, and terrain category. Furthermore, charts were developed for calculating the story shear force and story bending moments for the building heights varying from 100 to 250 m. The proposed new approach is one of the quick evaluators for dynamic along‐wind response of the tall buildings.

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“…Further, to present an innovative method for investigating the seismic vulnerability of the existing concrete structures with moment resisting frames (MRF) [26] and to generalize the across wind response of tall and slender buildings from the inadequate existing data so that across wind response can be attained for a building with specified (h:b:d) ratio [27], ANN was adopted. 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.…”

Section: Introductionmentioning

confidence: 99%

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

Paul

Dalui

2020

Period. Polytech. Civil Eng.

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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.

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Dynamic along wind response of tall buildings using Artificial Neural Network

Cited by 11 publications

References 12 publications

Dynamic wind response of tall buildings using artificial neural network

Dynamic wind response of tall buildings using artificial neural network

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

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

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