Real-Time Dynamic Earth-Pressure Regulation Model for Shield Tunneling by Integrating GRU Deep Learning Method With GA Optimization
Real-Time Dynamic Earth-Pressure Regulation Model for Shield Tunneling by Integrating GRU Deep Learning Method With GA Optimization
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DOI：
10.1109/access.2020.2984515
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发表时间：
2020-01-01
期刊：
影响因子：
3.9
通讯作者：
Zhou, Annan
中科院分区：
文献类型：
--
作者：
Gao, Min-Yu;Zhang, Ning;Zhou, Annan
本文提出了一个智能框架，预测和自动调节土压力，使用深度学习技术在土压力平衡盾构隧道施工。提出了一种新的成本函数（相对均方误差）与门控递归单元（GRU）相结合的预测模型。为了降低数据集的噪声，提高模型的精度，将滑动平均平滑方法引入GRU模型。将GRU模型与基于遗传算法的优化器相结合，提出了一种实时动态调节运行参数的模型。通过调整运行参数，动态调节模型将超压控制在建议范围内。提出的预测和调控模型应用于中国洛阳地铁隧道施工。结果表明，所提出的模型为隧道自动化施工提供了良好的指导。
This paper proposes an intelligent framework to predict and automatically regulate earth pressure using a deep learning technique during earth pressure balance shield tunneling. A prediction model was proposed by integrating a new cost function (relative mean square error) with a gated recurrent unit (GRU). The moving average smoothing method was also incorporated into the GRU model to reduce the noise of the dataset and improve the accuracy of the proposed model. A real-time dynamic regulation model for adjusting the operational parameters was proposed by integrating the GRU model into a genetic algorithm-based optimizer. By adjusting the operational parameters, the dynamic regulation model regulates the excessive predicted earth pressure within a suggested range. The proposed prediction and regulation models were applied to a metro tunnel construction in Luoyang, China. The results show that the proposed models provide good guidance for automated tunnel construction.