基于时空特征区域神经网络的施工隧道沉降量预测

针对隧道施工过程中沉降量精准预测问题, 提出了一种基于时空特征区域神经网络的施工隧道沉降量预测方法. 依据当前隧道地表下沉量, 通过有效融合多维空间特征量, 对未来的演化趋势做出合理预测. 以白家庄隧道栾川端的地表观测数据为例, 对所提方法的预测性能进行算例分析. 结果表明: 所提预测方法对隧道地表沉降量数据均有较准确的预测效果, 且预测结果也具有一定的鲁棒性. 研究可应用于实际隧道施工的监测管理过程.

Prediction of Ground Settlement Induced by Tunnel Construction with Neural Network Based on Spatiotemporal Feature Region

To achieve accurate prediction of settlement during tunnel construction, this study proposes a prediction method for settlement in construction tunnels through neural networks based on spatiotemporal feature region. This method effectively integrates multi-dimensional spatial characteristics and makes a reasonable prediction of the future evolution trend according to the current tunnel ground settlement. Taking the ground observation data at the Luanchuan end of Baijiazhuang Tunnel as an example, this study analyzes the prediction performance of the proposed method. The prediction results show that the sensing data of tunnel ground settlement is accurate and robust. The research can be applied to the monitoring and management process of actual tunnel construction.