With the increasing demand for deep and large-scale excavation pits, the deformation response during excavation has become exceedingly complex, especially located in building-intensive areas. This paper proposes a hybrid deep learning model named attention-causality-based graphical gated network (AC-GGN) to accurately make the spatio-temporal prediction about the excavation-induced ground settlement at different monitoring points during the foundation pit construction. The novelty of the AC-GGN model lies in its flexible integration of four key components, including the Granger causality (GC) test, graph convolutional network (GCN), the gated recurrent unit (GRU), and attention mechanisms, which work together to effectively capture casual relationships along with spatial and temporal dependence embedded in the observed time-series from each monitoring points and then boost the prediction performance. To validate its applicability and superiority, a case study about a metro station excavation project in the Shanghai Metro Line 14 is conducted. Results indicate that the AC-GGN model outperforms state-of-the-art algorithms, which can make precise predictions for each monitoring point. The proper data augmentation technique facilitates long-term prediction with high accuracy, thereby expanding the scope of AC-GGN application beyond short-term prediction. Moreover, the global sensitivity analysis can be used to reveal which monitoring points have the most significant impact on ground settlement prediction. It can aid in identifying key risk areas for monitoring and control. In summary, the novel architecture of AC-GGN is beneficial to dynamically capture and predict the trend of ground settlement across different areas of the construction site. Practically, the accurate prediction results generated by AC-GGN offer rich evidence to not only perceive the excavation-induced risk development but also formulate corresponding measures in advance for risk mitigation.

中文翻译：

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深基坑开挖引起的地面沉降的时空预测：考虑因果关系的混合图形网络方法

随着对深基坑和大型基坑的需求不断增加，基坑开挖过程中的变形响应变得异常复杂，尤其是在建筑物密集的地区。本文提出了一种名为基于注意因果关系的图形门控网络（AC-GGN）的混合深度学习模型，以准确地对基坑施工过程中不同监测点开挖引起的地面沉降进行时空预测。AC-GGN模型的新颖之处在于它灵活地集成了四个关键组件，包括格兰杰因果关系（GC）测试、图卷积网络（GCN）、门控循环单元（GRU）和注意力机制，它们共同作用有效捕获每个监测点观察到的时间序列中嵌入的偶然关系以及空间和时间依赖性，然后提高预测性能。为了验证其适用性和优越性，以上海地铁14号线地铁车站开挖工程为例进行研究。结果表明，AC-GGN 模型的性能优于最先进的算法，可以对每个监测点做出精确的预测。适当的数据增强技术有助于高精度的长期预测，从而将 AC-GGN 的应用范围扩展到短期预测之外。此外，全局敏感性分析可用于揭示哪些监测点对地面沉降预测影响最显着。它可以帮助识别关键风险领域以进行监测和控制。综上所述，AC-GGN 的新颖架构有利于动态捕获和预测施工现场不同区域的地面沉降趋势。实际上，AC-GGN产生的准确预测结果不仅可以为感知开挖引发的风险发展提供丰富的证据，还可以提前制定相应的风险缓解措施。