SHU-SUCG Research Centre for Building Industrialization, Shanghai University, Shanghai, 200072, China.
SHU-UTS SILC Business School, Shanghai University, Shanghai, 201800, China.
Sci Rep. 2023 Jan 31;13(1):1799. doi: 10.1038/s41598-023-28243-6.
Decision-making on shield construction parameters depends on timely and accurate geological condition feedback. Real-time mastering of geological condition around the shield during tunnelling is necessary to achieve safe and efficient construction. This paper proposes a Rapidly Geological Features Identification (RGFI) method that balances the model's generalizability and the accuracy of geological identification. First, a k-means algorithm is used to redefine the stratum based on the key mechanical indexes of strata. An XGBoost model is then used to determine the stratum composition of the excavation face based on the tunnelling parameters. If the result is compound strata, a deep neural network with an attention mechanism is used to predict the percentage of each stratum. The attention mechanism assigns weights to the features of the tunnelling parameters according to the stratum composition. The simulation results in the interval between Qian-Zhuang and Ke-Ning Road of Nanjing Metro show that the method can effectively determine the geological conditions on the excavation face. Furthermore, the method was used in the Hangzhou-Shaoxing intercity railroad tunnel project, where the 'ZhiYu' self-driving shield was used for tunnelling control. It helped the 'ZhiYu' shield to adjust the construction parameters quickly and improve the safety and quality of the project.
盾构施工参数的决策取决于对地质条件的及时、准确反馈。为实现安全高效施工，必须实时掌握盾构掘进过程中周围的地质条件。本文提出了一种 Rapidly Geological Features Identification（RGFI）方法，该方法在平衡模型通用性和地质识别准确性方面取得了较好的效果。首先，基于地层的关键力学指标，使用 k-means 算法重新定义地层。然后，根据掘进参数，使用 XGBoost 模型确定开挖面的地层组成。如果结果是复合地层，则使用具有注意力机制的深度神经网络来预测每个地层的百分比。注意力机制根据地层组成对掘进参数的特征赋予权重。在南京地铁钱-庄和科-宁路段的区间模拟结果表明，该方法可以有效地确定开挖面上的地质条件。此外，该方法还应用于杭州-绍兴城际铁路隧道项目中，使用“智驭”号盾构机进行掘进控制。该方法帮助“智驭”盾构快速调整施工参数，提高了项目的安全性和质量。
