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.

中文翻译：

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人工神经网络在高层建筑动态风向响应预测中的应用

本文提出了一种基于人工神经网络（ANN）的简化方法，用于预测印度风标准IS 875（第3部分）：2015中的动态顺风响应。该标准为特定方面提供了直接指南建筑物比（h / b比率为3、5、10和20）以评估顺风荷载。对于IS 875（第3部分）：2015中未指定的宽高比，动态风荷载及其响应的评估可能必须通过边界层风洞实验或其他计算技术进行估算。实验和计算方法在时间和所需资源方面都是昂贵的。另外，人工神经网络是一种有效且具有成本效益的计算技术，可用于估算建筑物的动态风响应。在本文中，提出了ANN预测估计值，用于计算高层建筑的动态沿风基础剪力和基础弯矩。使用最佳拟合ANN模型生成的宽高比（h / b）的范围在3到20之间。最后，这些响应被用来提出一个简化的经验公式，作为纵横比，边比，风速和地形类别的函数。此外，还开发了图表，用于计算建筑物高度从100到250 m不等的故事剪力和故事弯矩。提出的新方法是高层建筑动态顺风响应的快速评估器之一。