Wang Min, Ye Xiao-Wei, Jia Jin-Dian, Ying Xin-Hong, Ding Yang, Zhang Di, Sun Feng
Polytechnic Institute, Zhejiang University, Hangzhou 310058, China.
Department of Civil Engineering, Zhejiang University, Hangzhou 310058, China.
Sensors (Basel). 2024 Jan 29;24(3):866. doi: 10.3390/s24030866.
The confining pressure has a great effect on the internal force of the tunnel. During construction, the confining pressure which has a crucial impact on tunnel construction changes due to the variation of groundwater level and applied load. Therefore, the safety of tunnels must have the magnitude of confining pressure accurately estimated. In this study, a complete tunnel confining pressure time axis was obtained through high-frequency field monitoring, the data are segmented into a training set and a testing set. Using GRU and RNN models, a confining pressure prediction model was established, and the prediction results were analyzed. The results indicate that the GRU model has a fast-training speed and higher accuracy. On the other hand, the training speed of the RNN model is slow, with lower accuracy. The dynamic characteristics of soil pressure during tunnel construction require accurate prediction models to maintain the safety of the tunnel. The comparison between GRU and RNN models not only highlights the advantages of the GRU model but also emphasizes the necessity of balancing speed accuracy in tunnel construction confining pressure prediction modeling. This study is helpful in improving the understanding of soil pressure dynamics and developing effective prediction tools to promote safer and more reliable tunnel construction practices.
围压对隧道内力有很大影响。在施工过程中，对隧道施工至关重要的围压会因地下水位和施加荷载的变化而改变。因此，必须准确估算围压大小以确保隧道安全。在本研究中，通过高频现场监测获得了完整的隧道围压时间轴，并将数据分为训练集和测试集。利用门控循环单元（GRU）和循环神经网络（RNN）模型建立了围压预测模型，并对预测结果进行了分析。结果表明，GRU模型训练速度快且精度更高。另一方面，RNN模型训练速度慢且精度较低。隧道施工过程中土压力的动态特性需要精确的预测模型来确保隧道安全。GRU模型与RNN模型的比较不仅突出了GRU模型的优势，也强调了在隧道施工围压预测建模中平衡速度与精度的必要性。本研究有助于加深对土压力动态特性的理解，并开发有效的预测工具，以促进更安全、更可靠的隧道施工实践。
