Pavement distress situation prediction method based on graph neural network
A road pavement distress situation forecasting method employing graph convolutional networks was introduced,addressing the prediction problem of road pavement distress generation and deterioration.Firstly,a topological network was established through clustering algorithms,selecting the main influencing factors of the target pavement distress during its evolution.Subsequently,to enhance the expressive capability of the graph neural network for distress information,a graph topology enhancement method was employed,constructing views related to distress information from both static and dynamic aspects.Finally,an enhanced graph neural network (GNN) architecture was applied,by incorporating attention mechanisms in the view dimension to adjust the influence of different views and utilizing Transformer and GRU modules in the temporal dimension to enhance the predictive performance of the model for pavement distress states over extended time sequences.The internal calibration tests of the model,including ablation studies,multi-sample testing,and hyperparameter control group validation,demonstrated the applicability and stability of the proposed model.For the large and sparse pavement disease dataset,the mean absolute error of this model converged within 4.0,which was better than the results of the traditional prediction algorithms in terms of comprehensive performance.
highway maintenancepavement distressgraph neural networktime series forecastcrack
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