Prediction of river embankment deformation during pipe jacking based on machine learning
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摘要: 过江顶管由于施工作业面较小，江边堆填荷载较大，影响江堤稳定性，提前预测江堤位移对安全施工具有重要意义。通过自适应噪声的完备集合经验模态分解方法（CEEMDAN），将非线性非稳定性的监测数据分解为趋势项和周期项，提出一种适合于江堤变形的智能预测模型（CTG）。模型针对趋势项采用多项式函数预测；针对周期项，采用结合粒子群优化算法和时间卷积网络−门控循环单元混合模型的PSO-TCN-GRU算法进行预测。将算法应用于段塘顶管施工项目，结果表明该智能算法优于其他经典的神经网络预测方法，具有一定的推广价值，可为同类工程变形预测提供参考。另外，建议进行监测数据预测时先进行数据分解和特征提取，有利于改善模型预测结果。
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关键词:
- 位移预测 /
- 顶管施工 /
- 完备集合经验模态分解 /
- 时间卷积网络−门控循环单元混合模型 /
- 粒子群算法 /
- 监测预警
Abstract: Pipe jacking has a significant impact on the stability of river embankments due to its small working surface and high stacking load. The early prediction of river embankment deformation is of great significance for safe construction. The Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) method was employed to decompose the nonlinear and unstable monitoring data into trend and periodic terms, and an intelligent prediction model (CTG) suitable for river embankment deformation was proposed. The model used a polynomial function to predict the trend item, and a time convolution network-gated recurrent unit hybrid model combined with a particle swarm optimization algorithm (i.e., PSO-TCN-GRU algorithm) was used to predict the periodic term. It was found that the intelligent algorithm proposed is superior to other classical neural network prediction methods, and has a certain promotional value, which can provide reference for the deformation prediction of similar projects. In addition, it is suggested that data decomposition and feature extraction should be carried out before monitoring data prediction, which is conducive to improving the prediction results of the model.-
Key words:
- displacement prediction /
- pipe jacking method /
- CEEMDAN /
- TCN-GRU /
- PSO /
- monitoring and warning
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表 1 水平位移趋势项模型预测参数
参数 a0 a1 a2 a3 a4 a5 值 −9.34 1.89×10−3 −1.56×10−5 9.08×10−9 −1.52×10−12 0 
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表 2 各类模型预测误差
模型 预测1 预测2 预测3 预测4 本文 0.0047 0.0083 0.0018 0.0079 BP 0.0121 0.0258 0.0202 0.0440 GRU 0.0099 0.0181 0.0073 0.0202 LSTM 0.0101 0.0201 0.0058 0.0237 TCN-GRU 0.0115 0.0225 0.0076 0.0402
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