Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Spatio-temporal prediction of deep excavation-induced ground settlement: A hybrid graphical network approach considering causality
Highlights A hybrid graphical network approach considering causality named AC-GGN is proposed. It can accurately make spatio-temporal prediction of deep excavation-induced ground settlement. It is verified in a practical excavation case to reach high accuracy for each monitoring point. Data augmentation techniques help to strengthen the long-term prediction ability of AC-GGN. Global sensitivity analysis is conducted to quantitatively explain prediction behavior of AC-GGN.
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.
Spatio-temporal prediction of deep excavation-induced ground settlement: A hybrid graphical network approach considering causality
Highlights A hybrid graphical network approach considering causality named AC-GGN is proposed. It can accurately make spatio-temporal prediction of deep excavation-induced ground settlement. It is verified in a practical excavation case to reach high accuracy for each monitoring point. Data augmentation techniques help to strengthen the long-term prediction ability of AC-GGN. Global sensitivity analysis is conducted to quantitatively explain prediction behavior of AC-GGN.
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.
Spatio-temporal prediction of deep excavation-induced ground settlement: A hybrid graphical network approach considering causality
Zhou, Xiaojing (Autor:in) / Pan, Yue (Autor:in) / Qin, Jianjun (Autor:in) / Chen, Jin-Jian (Autor:in) / Gardoni, Paolo (Autor:in)
14.01.2024
Aufsatz (Zeitschrift)
Elektronische Ressource
Englisch
Neural-network-based regression model of ground surface settlement induced by deep excavation
| British Library Online Contents | 2004
Neural-network-based regression model of ground surface settlement induced by deep excavation
| Online Contents | 2004