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Abstract

To correctly evaluate the service status of prefabricated hollow slab girder bridge, based on the measured data of apparent disease and load test of hollow slab girder bridge in reconstruction and expansion project, this paper first analyzed the data characteristics of the beam body, hinge joints, and support disease samples and obtained their statistical distribution rules. Then, according to the typical diseases (transverse cracks and hinge cracks at the bottom of the beam), the BP neural network was used to build a correlation model for them, and the close correlation between the two typical diseases of hollow slab girder bridge was proven. The transverse force transfer performance of hinge joints was used as the evaluation index of the transverse load distribution ability of the hollow slab girder bridge. Based on the difference between the measured joint height and the theoretical joint height in good condition, an evaluation index of hinge joint damage degree was established. Finally, based on the field bridge detection, test, and finite element simulation data, the neural network model of “apparent disease–transverse load distribution ability” correlation analysis was constructed. The results show that the neural network model can be used to evaluate the transverse load distribution ability of hollow slab girder bridges based on apparent disease and has good engineering practical value.

Publication Date

7-14-2023

DOI

10.14048/j.issn.1671-2579.2023.03.018

First Page

114

Last Page

121

Submission Date

March 2025

Reference

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