Ultimate Bending Strength Evaluation of MVFT Composite Girder by using Finite Element Method and Machine Learning Regressors

Abstract

This paper has evaluated the bending performance of a novel prefabricated MVFT steel-concrete composite girder. 9 meters pilot MVFT girder was analyzed by validated finite element model. In the pilot test, the height of web, the length of grouted concrete in the girder and net spacing between webs were parametrically modeled to discuss their effect to the bending strength. An ultimate bending strength formula has been obtained, which was based on the regression of parametric results. In the meantime, the two Machine Learning (ML) models, BP neural network and Least Squares Support Vector Machine, have been also implemented to train and then predict the ultimate strength of MVFT girder. Three factors were selected as input in ML models: the distance between steel girder’s Tensile Centroid(TC) and slab’s Compressive Centroid(CC), the distance between steel girder’s TC and its CC, the compressive area of steel girder. After the completion of the ML training, the ultimate strength predictions of 30 meters MVFT girder by BP model and the formula have been compared, which agrees well with each other and validates their accuracy.