Structural Engineering and Mechanics

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Comparison of regression model and LSTM-RNN model in predicting deterioration of prestressed concrete box girder bridges

  • Gao Jing (School of Architecture and Civil Engineering, Xiamen University) ;
  • Lin Ruiying (Xiamen Road and Bridge Engineering Investment Development Co. LTD) ;
  • Zhang Yao (School of Architecture and Civil Engineering, Xiamen University)
  • Received : 2023.04.17
  • Accepted : 2024.06.21
  • Published : 2024.07.10
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Abstract

Bridge deterioration shows the change of bridge condition during its operation, and predicting bridge deterioration is important for implementing predictive protection and planning future maintenance. However, in practical application, the raw inspection data of bridges are not continuous, which has a greater impact on the accuracy of the prediction results. Therefore, two kinds of bridge deterioration models are established in this paper: one is based on the traditional regression theory, combined with the distribution fitting theory to preprocess the data, which solves the problem of irregular distribution and incomplete quantity of raw data. Secondly, based on the theory of Long Short-Term Memory (LSTM) Recurrent Neural Network (RNN), the network is trained using the raw inspection data, which can realize the prediction of the future deterioration of bridges through the historical data. And the inspection data of 60 prestressed concrete box girder bridges in Xiamen, China are used as an example for validation and comparative analysis, and the results show that both deterioration models can predict the deterioration of prestressed concrete box girder bridges. The regression model shows that the bridge deteriorates gradually, while the LSTM-RNN model shows that the bridge keeps great condition during the first 5 years and degrades rapidly from 5 years to 15 years. Based on the current inspection database, the LSTM-RNN model performs better than the regression model because it has smaller prediction error. With the continuous improvement of the database, the results of this study can be extended to other bridge types or other degradation factors can be introduced to improve the accuracy and usefulness of the deterioration model.

Keywords

Acknowledgement

The authors would like to acknowledge the financial support from the Fundamental Research Funds for the Central Universities (20720220070).

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