Prognosis of Fatigue Damage in Steel Bridge Decks Based on Long Short-Term Memory Neural Networks

MA Kaichen; XU Yangming; JIANG Linsong; XING Song; WANG Xie

doi:10.3724/j.gyjzG24060304

Volume 55 Issue 7

Jul. 2025

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MA Kaichen, XU Yangming, JIANG Linsong, XING Song, WANG Xie. Prognosis of Fatigue Damage in Steel Bridge Decks Based on Long Short-Term Memory Neural Networks[J]. INDUSTRIAL CONSTRUCTION, 2025, 55(7): 109-118. doi: 10.3724/j.gyjzG24060304

Citation:

MA Kaichen, XU Yangming, JIANG Linsong, XING Song, WANG Xie. Prognosis of Fatigue Damage in Steel Bridge Decks Based on Long Short-Term Memory Neural Networks[J]. INDUSTRIAL CONSTRUCTION, 2025, 55(7): 109-118. doi: 10.3724/j.gyjzG24060304

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Prognosis of Fatigue Damage in Steel Bridge Decks Based on Long Short-Term Memory Neural Networks

doi: 10.3724/j.gyjzG24060304

Bay Area Great Bridge Maintenance Technology Center of Guangdong Highway Construction Co., Ltd., Guangzhou 510120, China

Received Date: 2024-06-03
Available Online: 2025-09-12

Abstract

Abstract

Orthotropic steel decks are prone to fatigue damage under repeated traffic loads， which poses a serious threat to the safe and healthy operation of bridges. By using large-scale bridge health monitoring data， the fatigue damage of orthotropic steel bridge decks can be predicted. In this paper， a fatigue damage prognosis method for steel bridge decks based on long short-term memory （LSTM） neural network is proposed. This method enables long-term fatigue damage prognosis of steel bridge decks， allowing early assessment of bridge health status and providing an effective reference for fatigue evaluation of steel bridges. First， the characteristics of LSTM neural networks and fatigue damage sequences were studied and analyzed. Fatigue damage sequences are essentially a type of time series， making them highly suitable for modeling and prediction using LSTM neural networks. The stress characteristics and damage sequence characteristics of four measuring points were analyzed for two types of fatigue details： roof and longitudinal rib welding details， and transverse partition and longitudinal rib cross structural details. The hourly damage sequence was selected as the dataset for training. The effects of the number of hidden layers of the model， the number of neurons in the hidden layer， the output step-size of the prediction， the size of the dataset， the model prediction method and the model update method on the prediction accuracy of the model were explored. A neural network with LSTM and long term memory was established to realize the accurate prediction of the fatigue damage after one month of the two fatigue details.
- steel bridge,
- long short-term memory neural networks,
- fatigue damage,
- prognosis of fatigue damage

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References(24)

References

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