Prediction of Maximum Story Drift of MDOF Structures under Simulated Wind Loads Using Artificial Neural Networks

• Main Authors: Omar Payán-Serrano, Edén Bojórquez, Juan Bojórquez, Robespierre Chávez, Alfredo Reyes-Salazar, Manuel Barraza, Arturo López-Barraza, Héctor Rodríguez-Lozoya, Edgar Corona
• Format: Article
• Language: English
• Published: MDPI AG 2017-05-01
• Series: Applied Sciences
• Subjects: dynamic analysis; wind simulation; artificial neural networks; parallel computing
• Online Access: http://www.mdpi.com/2076-3417/7/6/563

The aim of this paper is to investigate the prediction of maximum story drift of Multi-Degree of Freedom (MDOF) structures subjected to dynamics wind load using Artificial Neural Networks (ANNs) through the combination of several structural and turbulent wind parameters. The maximum story drift of 1600 MDOF structures under 16 simulated wind conditions are computed with the purpose of generating the data set for the networks training with the Levenberg–Marquardt method. The Shinozuka and Newmark methods are used to simulate the turbulent wind and dynamic response, respectively. In order to optimize the computational time required for the dynamic analyses, an array format based on the Shinozuka method is presented to perform the parallel computing. Finally, it is observed that the already trained ANNs allow for predicting adequately the maximum story drift with a correlation close to 99%.