Spatio-temporal prediction of deep excavation-induced ground settlement: A hybrid graphical network approach considering causality

Xiaojing Zhou, Yue Pan, Jianjun Qin, Jin Jian Chen, Paolo Gardoni

Research output: Contribution to journalArticlepeer-review

Abstract

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.

Original languageEnglish (US)
Article number105605
JournalTunnelling and Underground Space Technology
Volume146
DOIs
StatePublished - Apr 2024
Externally publishedYes

Keywords

  • Causality discovery
  • Deep excavation
  • Graphical and gated recurrent neural network
  • Ground settlement prediction
  • Spatio-temporal pattern capture

ASJC Scopus subject areas

  • Building and Construction
  • Geotechnical Engineering and Engineering Geology

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