摘要：

桥梁作为重要的交通基础设施, 质量是其建造及使用的重中之重。沉井作为基础结构, 在桥梁建造中被广泛应用。在沉井建造过程中, 实时准确的下沉姿态预测有助于降低事故风险, 提高工程质量。常用的统计模型和机器学习模型等预测方法, 难以应对时序数据的非线性时空特征, 如结构应力、沉井下沉姿态等, 导致预测结果不准确。深度学习模型可以捕获数据时空特征, 已广泛应用于时序数据预测, 但尚未被应用于沉井下沉姿态预测等相关任务。因此, 提出基于图神经网络(GNN)的多指标预测模型(MiPM)。MiPM通过自注意力机制以及门控循环单元(GRU)动态建立时序数据序列间的图邻接矩阵, 并结合卷积神经网络提取时序数据的时间特征和空间特征。通过交错的网络结构融合时空特征, 映射预测结果。为验证MiPM的有效性, 以一个真实的桥梁沉井建造工程为实证研究案例。实验结果表明, 对比13个基线模型, MiPM在均方根误差指标上至少降低5.6%, 具有更好的预测结果表现。

关键词: 图神经网络, 图邻接矩阵, 沉井建造, 姿态预测, 时序数据

Abstract:

The quality of bridges, as crucial transportation infrastructure, is an important consideration during its construction and usage. Open caissons are widely used as basic structures in bridge construction. During the open caisson construction process, real-time and accurate prediction of the sinking attitude helps reduce accident risks and may improve project quality. Commonly used prediction methods, such as statistical models and machine learning models, have difficulty coping with the nonlinear spatiotemporal features of time series data, such as structural stress and open caisson sinking attitude, resulting in inaccurate prediction results. Deep learning models can capture the spatiotemporal features of data and have been widely used in time-series data prediction. However, they have not yet been applied to related tasks, such as open caisson sinking attitude prediction. In this study, we propose a Multi-indicator Prediction Model (MiPM) based on a Graph Neural Network (GNN). MiPM dynamically establishes the graph adjacency matrix between time-series data sequences using the self-attention mechanism and the Gate Recurrent Unit (GRU) and combines it with the convolutional neural network to extract the temporal and spatial features of time-series data. The spatiotemporal features are fused using an interleaved network structure, and the prediction results are mapped. To verify the effectiveness of the MiPM, a real open-caisson bridge construction project is used as an empirical case study. The results show that, compared with 13 baseline models, MiPM has a better prediction performance; for example, its Root Mean Square Error (RMSE) is at least 5.6% lower.

Key words: Graph Neural Network (GNN), graph adjacency matrix, open caisson construction, attitude prediction, time series data