Indirect Identification and Analysis of Bridge Damage Using Vehicle–Bridge Coupled Vibration and Deep Learning
Publication: Journal of Performance of Constructed Facilities
Volume 38, Issue 4
Abstract
This study addresses the limitations of existing indirect bridge damage identification methods that are based on the vehicle–bridge coupled vibration theory of highway bridges. To overcome these shortcomings, we propose an extended approach that incorporates various types of deep-learning models with vehicle–bridge coupled vibration responses. The proposed method is demonstrated using a three-span continuous beam bridge as a case study. First, a vehicle and bridge analysis model is established, and bridge damage is simulated using unit stiffness reduction, considering different damage scenarios. Next, to account for road roughness randomness, vehicle–bridge coupling vibration analysis is performed under various road roughness conditions, yielding the vertical acceleration vibration signal of the vehicle. Subsequently, we employ an end-to-end damage recognition method, utilizing the vehicle acceleration response as the network input, to construct two types of deep-learning models: one-dimensional convolutional neural network (1D-CNN) and convolutional long short-term memory neural network (CNN-LSTM). The recognition performance of both models is compared and analyzed. Taking Zhengzhou Taohuayu Self-Anchored Suspension Bridge in China as an example, this study delves into the capability of bridge damage identification using deep learning. The results demonstrate that the one-dimensional convolutional neural network achieves excellent recognition performance in terms of both damage location and severity.
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Data Availability Statement
Some or all data, models, or code that support the findings of this study are available from the corresponding author upon reasonable request.
Acknowledgments
We would like to express our gratitude for the financial support provided by the National Natural Science Foundation of China (51408557), the China Postdoctoral Science Foundation (2013M541995), and the Program of the Department of Transportation of Henan Province (2020J-2-6), which has made this study possible.
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© 2024 American Society of Civil Engineers.
History
Received: Nov 14, 2023
Accepted: Feb 26, 2024
Published online: May 8, 2024
Published in print: Aug 1, 2024
Discussion open until: Oct 8, 2024
ASCE Technical Topics:
- Artificial intelligence and machine learning
- Bridge engineering
- Bridge management
- Bridge-vehicle interaction
- Bridges
- Bridges (by type)
- Computer programming
- Computing in civil engineering
- Continuum mechanics
- Coupling
- Dynamics (solid mechanics)
- Engineering mechanics
- Highway bridges
- Highway transportation
- Infrastructure
- Motion (dynamics)
- Neural networks
- Solid mechanics
- Structural engineering
- Structural members
- Structural systems
- Suspension bridges
- Transportation engineering
- Vehicles
- Vibration
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