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| Pavement distress situation prediction method based on graph neural network |
Zechao MA1( ),Xiaoming LIU1,*(),Hanqing XIA2,Weiqiang WANG1,Jiuzeng WANG3,Haitao SHEN3 |
1. School of Electrical and Control Engineering, North China University of Technology, Beijing 100144, China
2. College of Civil Aviation, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, China
3. Tangshan Expressway Group Company, Tangshan 063000, China |
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Abstract 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.
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Received: 23 October 2023
Published: 25 November 2024
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| Fund: 北方工业大学毓秀创新项目 (2024NCUTYXCX109). |
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Corresponding Authors:
Xiaoming LIU
E-mail: z2mazc@126.com;tslxm@sina.com
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基于图神经网络的路面病害态势预测方法
针对路面病害生成和恶化的预测问题，提出应用图卷积神经网络的路面病害态势预测方法. 通过聚类算法建立拓扑网络，选取目标病害在演化过程中的主要影响因素；为了增强图神经网络对病害信息的表达能力，采用图拓扑增强的方法，从静态和动态方面分别构造与病害信息相关的视图；采用图神经网络(GNN)架构增强的方法，在视图维度上应用注意力机制调整不同视图的影响力，并在时间维度上应用Transformer和GRU模块，增强模型在长时间序列中对病害状态的预测性能. 设计模型的内部调整测试，经消融试验、多样本测试和超参数对照组的验证，证明所提模型的适用性和稳定性. 针对大型稀疏的路面病害数据集，此模型的平均绝对误差均值收敛在4.0以内，综合性能优于传统预测算法.
关键词：
公路养护,
路面病害,
图神经网络,
时间序列预测,
裂缝
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