Discontinuities in rock mass are the characteristic challenge of rock tunnel engineering projects, which have a vital impact on rock mass exposures' mechanical and hydrological characteristics. There is a growing demand for predicting the fracture maps during tunnel excavation to ensure a smooth tunnel excavation process. The computer vision measurement of fractures in the tunnel surface is a current hot spot, but the traditional statistical analysis methods for fractures are still mainstream. This paper uses a novel perspective of the time-space sequence to explain the continuously exposed rock mass during tunneling. A spatial-aware recurrent neural network is proposed, which takes the historical fracture maps as the input to predict the unexcavated part. The experimental results suggest that the proposed model produces reliable performance and is superior to the other two state-of-the-art deep learning models. Moreover, the test on the site rock tunnel data suggested promising results for fracture map predictions.

中文翻译：

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基于时空图像序列建模的岩体断裂图预测

岩体的不连续性是岩石隧道工程项目面临的典型挑战，对岩体暴露的力学和水文特征具有重要影响。人们越来越需要预测隧道开挖过程中的裂缝图，以确保隧道开挖过程顺利进行。隧道面层裂缝的计算机视觉测量是当前的研究热点，但传统的裂缝统计分析方法仍是主流。本文使用时空序列的新视角来解释隧道开挖过程中不断暴露的岩体。提出了一种空间感知的递归神经网络，它将历史裂缝图作为输入来预测未开挖部分。实验结果表明，所提出的模型产生了可靠的性能，并且优于其他两个最先进的深度学习模型。此外，对现场岩石隧道数据的测试为裂缝图预测提供了有希望的结果。