Wind-Induced Dynamic Critical Response in Buildings Using Machine Learning Techniques

Abstract

Wind is one of the main factors causing variable actions in tall buildings, and its effects cannot be neglected in the evaluation of either displacements and accelerations that develop in the structure or the internal forces generated indirectly within. However, the structural analyses necessary for these evaluations usually lead to high computational efforts, so surrogate models have been increasingly used to reduce the computational time required. In this work, five machine learning techniques are evaluated for predicting maximum displacement in buildings under dynamic wind loads: k-nearest neighbors (kNN), random forest (RF), support vector regression (SVR), Gaussian process regression (GPR), and artificial neural network (ANN). An initial dataset with 500 random samples was used to evaluate the responses generated by the models. The predictor variables were the building’s height, width, and length; average density; damping ratio; wind velocity; and ground roughness. The obtained results demonstrate that the techniques can predict dynamic responses, mainly the GPR and the ANN.

Keywords

• wind load
• dynamic analysis
• machine learning
• artificial neural network
• Gaussian process regression
• uncertainty quantification