Machine Learning for Prediction of Wind Effects on Behavior of a Historic Truss Bridge скачать в хорошем качестве

Machine Learning for Prediction of Wind Effects on Behavior of a Historic Truss Bridge

Presented By: Mohamed Issa, University of Colorado Description: This presentation discusses the behavior of a truss bridge under wind loading. To examine the wind-related responses of the bridge, state-of-the-art and traditional modeling methodologies are employed: a machine learning approach called random forest and three-dimensional finite element analysis. Upon training and validating these modeling methods using experimental data collected from the field, member-level forces and stresses are predicted in tandem with wind speeds inferred by Weibull distributions. The intensities of the in-situ wind are dominated by the location of sampling, and the degree of partial fixities at the supports of the truss system is found to be insignificant.