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A platform for research: civil engineering, architecture and urbanism
Spatial-temporal fusion network for maximum ground surface settlement prediction during tunnel excavation
Abstract The maximum ground surface settlement prediction is a complex problem as the settlement depends on plenty of intrinsic and extrinsic factors. To obtain the approximate range of the settlement, a hybrid prediction dataset including the geological and construction parameters is built using spatial and temporal series according to the sampling methods. The settlement prediction task is transformed into a multi-modal and multi-variate series prediction task. Hence, a spatial-temporal fusion network (STF-Network) is proposed. The spatial-temporal fusion mechanism is firstly designed to establish the spatial-temporal fusion map, which makes spatial and temporal series interact earlier. Then, the 3D residual unit structure is designed to capture the features of temporal series and spatial-temporal fusion map, and two fully-connected layers are established to capture the spatial structural information. Finally, the final output is merged by the three components. The experimental results for STF-Network demonstrate the superiority over state-of-the-art methods.
Highlights Establishing a hybrid prediction dataset using spatial and temporal series Designing a spatial-temporal fusion network (STF-Network) for settlement prediction Employing STFM make spatial and temporal series interact earlier Designing 3D-ResUnit structure based on the periodicity of the temporal series data
Spatial-temporal fusion network for maximum ground surface settlement prediction during tunnel excavation
Abstract The maximum ground surface settlement prediction is a complex problem as the settlement depends on plenty of intrinsic and extrinsic factors. To obtain the approximate range of the settlement, a hybrid prediction dataset including the geological and construction parameters is built using spatial and temporal series according to the sampling methods. The settlement prediction task is transformed into a multi-modal and multi-variate series prediction task. Hence, a spatial-temporal fusion network (STF-Network) is proposed. The spatial-temporal fusion mechanism is firstly designed to establish the spatial-temporal fusion map, which makes spatial and temporal series interact earlier. Then, the 3D residual unit structure is designed to capture the features of temporal series and spatial-temporal fusion map, and two fully-connected layers are established to capture the spatial structural information. Finally, the final output is merged by the three components. The experimental results for STF-Network demonstrate the superiority over state-of-the-art methods.
Highlights Establishing a hybrid prediction dataset using spatial and temporal series Designing a spatial-temporal fusion network (STF-Network) for settlement prediction Employing STFM make spatial and temporal series interact earlier Designing 3D-ResUnit structure based on the periodicity of the temporal series data
Spatial-temporal fusion network for maximum ground surface settlement prediction during tunnel excavation
Chen, Liang (author) / Hashiba, Kimihiro (author) / Liu, Zhitao (author) / Lin, Fulong (author) / Mao, Weijie (author)
2022-12-25
Article (Journal)
Electronic Resource
English
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