Prediction of Effective Width of Varying Depth Box-Girder Bridges Using Convolutional Neural Networks

Effective flange width is widely used in bridge design to consider the effect of shear lag. The simplified formula for the effective flange width of box girder bridges of variable depth in existing codes and studies may not be conservative, and accurate methods, such as the finite element method, ar...

Full description

Saved in:
Bibliographic Details
Published in:Advances in civil engineering 2022-03, Vol.2022 (1)
Main Authors: Hu, Kejian, Wu, Xiaoguang
Format: Article
Language:English
Subjects:
Artificial neural networks
Box girder bridges
Box girders
Bridge design
Bridges
Civil engineering
Concrete
Datasets
Deep learning
Design specifications
Finite element analysis
Finite element method
Mathematical models
Neural networks
Prestressed concrete
Stress concentration
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Effective flange width is widely used in bridge design to consider the effect of shear lag. The simplified formula for the effective flange width of box girder bridges of variable depth in existing codes and studies may not be conservative, and accurate methods, such as the finite element method, are time-consuming. The purpose of this research is to develop a method that uses a convolutional neural network (CNN) to predict the effective width of box girder bridges of varying depths. These models have been trained, validated, and tested on datasets generated from thousands of finite element models. The lower error in the test set indicates that the CNN model can be used to predict the effective width. In addition, the impact of different architectures is also studied. The proposed method makes real-time analysis possible and has a wide range of applications in the analysis and design of box-girder bridges at different depths.
ISSN:1687-8086
1687-8094
DOI:10.1155/2022/4617392