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Probability analysis on tunnels in heterogeneous strata based on borehole data-driven conditional random fields and convolutional neural network
Tunnelling and Underground Space Technology ( IF 6.7 ) Pub Date : 2025-01-18 , DOI: 10.1016/j.tust.2025.106402
Gaoyu Ma, Chuan He, Zhengshu He, Rongmin Bai, Guowen Xu
Tunnelling and Underground Space Technology ( IF 6.7 ) Pub Date : 2025-01-18 , DOI: 10.1016/j.tust.2025.106402
Gaoyu Ma, Chuan He, Zhengshu He, Rongmin Bai, Guowen Xu
Tunnels in heterogeneous strata always encounter spatially varied geological formations, causing asymmetric responses and localized failure in the supporting structure. The homogeneity assumption for surrounding strata, commonly adopted in tunnel design and construction, will neglect the inherent spatial uncertainty of rock mass and lead to the overestimation in tunnel bearing capacity. The conventional stochastic calculations for analyzing tunnel performance in heterogeneous strata also fail to reflect the statistical asymmetry in mechanical behaviors of supporting structure. With the application of mechanized equipment with built-in sensors in drilling and blasting construction, rock parameters at borehole locations can be promptly derived through the drilling data. This systematic on-site monitoring necessitates a rational and stationary extrapolation using rock parameters from the excavation face to the surrounding strata, as the inversion results provide a more precise depiction of the properties of surrounding strata and enable the dynamic design for supporting structure during construction. Therefore, an innovative approach was proposed in this research to conduct probability analysis on the mechanical behaviors of tunnels in heterogeneous strata based on conditional random field models. The statistical characteristics of random variables in these fields were constrained by the derived rock parameters on the excavation face using Hoffman method. The probability distributions of mechanical behaviors were analyzed for tunnels with both symmetric and asymmetric anchor cable systems. In addition, a trained convolutional neural network (CNN) model was implemented to reduce the computational resources required in massive numerical simulations. The tunnel deformation at different circumferential locations can be predicted with an acceptable accuracy and minimal time consumption that significantly facilitated the probabilistic assessments.
中文翻译:
基于井眼数据驱动的条件随机场和卷积神经网络的非均质地层隧道概率分析
非均质地层中的隧道总是遇到空间变化的地质构造,导致支撑结构的不对称响应和局部失效。隧道设计和施工中通常采用的周围地层均匀性假设会忽略岩体固有的空间不确定性,导致隧道承载力被高估。用于分析非均质地层中隧道性能的常规随机计算也无法反映支撑结构力学行为的统计不对称性。随着在钻孔和爆破施工中应用带有内置传感器的机械化设备,可以通过钻孔数据及时得出钻孔位置的岩石参数。这种系统的现场监测需要使用岩石参数从开挖面到周围地层进行合理和平稳的外推,因为反演结果可以更精确地描述周围地层的特性,并能够在施工期间对支撑结构进行动态设计。因此,本研究提出了一种基于条件随机场模型对非均质地层中隧道力学行为进行概率分析的创新方法。这些场中随机变量的统计特征受 Hoffman 方法在开挖面上推导的岩石参数的约束。分析了具有对称和非对称锚索系统的隧道的力学行为的概率分布。此外,还实施了经过训练的卷积神经网络 (CNN) 模型,以减少大规模数值模拟所需的计算资源。 可以以可接受的精度和最小的时间消耗预测不同圆周位置的隧道变形,这极大地促进了概率评估。
更新日期:2025-01-18
中文翻译:
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基于井眼数据驱动的条件随机场和卷积神经网络的非均质地层隧道概率分析
非均质地层中的隧道总是遇到空间变化的地质构造,导致支撑结构的不对称响应和局部失效。隧道设计和施工中通常采用的周围地层均匀性假设会忽略岩体固有的空间不确定性,导致隧道承载力被高估。用于分析非均质地层中隧道性能的常规随机计算也无法反映支撑结构力学行为的统计不对称性。随着在钻孔和爆破施工中应用带有内置传感器的机械化设备,可以通过钻孔数据及时得出钻孔位置的岩石参数。这种系统的现场监测需要使用岩石参数从开挖面到周围地层进行合理和平稳的外推,因为反演结果可以更精确地描述周围地层的特性,并能够在施工期间对支撑结构进行动态设计。因此,本研究提出了一种基于条件随机场模型对非均质地层中隧道力学行为进行概率分析的创新方法。这些场中随机变量的统计特征受 Hoffman 方法在开挖面上推导的岩石参数的约束。分析了具有对称和非对称锚索系统的隧道的力学行为的概率分布。此外,还实施了经过训练的卷积神经网络 (CNN) 模型,以减少大规模数值模拟所需的计算资源。 可以以可接受的精度和最小的时间消耗预测不同圆周位置的隧道变形,这极大地促进了概率评估。