DNN-Based Estimation of the Maximum Lateral Flange Moments of Horizontally Curved I-Girder Bridges

Abstract

Horizontally curved I-girder bridges are known to be complex. Bending and torsion forces are imposed on the bridges owing to their shapes with initial curvatures. This torsion is a combination of pure and warping forces. The horizontally curved I-girder is significantly affected by warping behavior, which decreases the bending rigidity of its member. To investigate the warping behavior of the horizontally curved I-girder bridges a finite element analysis (FEA) must be performed. In this study, an FEA was performed to investigate the warping torsional behavior of a horizontally curved I-girder bridge, and a structural response database was obtained. Based on the database, the least absolute shrinkage and selection operator was employed to select features affecting the warping behavior. Subsequently, deep neural network models were trained with selected features for an input layer and maximum lateral flange moment data for an output layer. Several models were constructed and compared according to the number of hidden layers and neurons, and the model with the highest performance was proposed. Finally, it was confirmed that the estimated lateral flange moments computed by the proposed model showed a good correlation with the FEA results.