Research on Bridge Concrete Crack Damage Prediction Method Based on Deep Learning Temporal Model SEU: Southeast University

Format:

Book

Conference/Event

Author/Creator:

Xu, Weidong, author.

Contributor:

Cai, C.S.

Xiong, Wen

Zhu, Yanjie

Conference Name:

2024 International Conference on Smart Transportation Interdisciplinary Studies (2024-12-13 : Nanjing, China)

Language:

English

Physical Description:

1 online resource cm

Place of Publication:

Warrendale, PA SAE International 2025

Summary:

Intelligent Structural Health Monitoring (SHM) of bridge is a technology that utilizes advanced sensor technology along with professional bridge engineering knowledge, coupled with machine vision and other intelligent methods for continuously monitoring and evaluating the status of bridge structures. One application of SHM technology for bridges by way of machine learning is in the use of damage detection and quantification. In this way, changes in bridge conditions can be analyzed efficiently and accurately, ensuring stable operational performance throughout the lifecycle of the bridge. However, in the field of damage detection, although machine vision can effectively identify and quantify existing damages, it still lacks accuracy for predicting future damage trends based on real-time data. Such shortfall l may lead to late addressing of potential safety hazards, causing accelerated damage development and threatening structural safety. To tackle this problem, this study designs a deep learning model based on temporal information to solve the problem of predictive damage development, achieving early warning and dynamic evaluation effects. This study focuses on concrete crack development, and the CrackAE model is based on traditional semantic segmentation models and conditional autoencoder architecture. The model consists of an encoder and a decoder. The encoder accepts image data and outputs a feature map. The future map along with the conditional vector encoded based on physical temporal information, serves as the input to the decoder. The output of decoder is the development state of the crack at the specified prediction time. The model achieved an accuracy of 94.6% in real bending failure tests of concrete beams, indicating that the model meets high-precision prediction requirements. This validates the feasibility of deep learning in predicting damage development and provides new ideas for data collection and prediction in actual bridge maintenance

Notes:

Vendor supplied data

Publisher Number:

2025-01-7126

Access Restriction:

Restricted for use by site license