Spatiotemporal deep learning approach on estimation of diaphragm wall deformation induced by excavation
This paper proposes a convolution neural network (CNN) based prediction method for concrete diaphragm wall (CDW) deflections. CNN algorithm is modified for processing the CDW deformation data collected from in-situ measurement in both time and space dimensions, and capable of making dynamic predicti...
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| Published in: | Acta geotechnica Vol. 16; no. 11; pp. 3631 - 3645 |
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| Main Authors: | , , , , |
| Format: | Journal Article |
| Language: | English |
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Berlin/Heidelberg
Springer Berlin Heidelberg
01.11.2021
Springer Nature B.V |
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| ISSN: | 1861-1125, 1861-1133 |
| Online Access: | Get full text |
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| Abstract | This paper proposes a convolution neural network (CNN) based prediction method for concrete diaphragm wall (CDW) deflections. CNN algorithm is modified for processing the CDW deformation data collected from in-situ measurement in both time and space dimensions, and capable of making dynamic prediction based on the extracted spatiotemporal features of wall deflection. The proposed method is validated through investigating a project of deep excavation in Suzhou, China. The predicted results show excellent agreement with field measurement and yield mean absolute errors of 0.86 mm and 1.55 mm for nowcasting and forecasting tasks, respectively. Three prevailing algorithms in time series prediction, namely, back propagation neural network, long short-term memory and autoregressive integrated moving average, are conducted for comparison. The results illustrate that the CNN outperforms the other algorithms in terms of accuracy and execution time. Therefore, the proposed CNN model is the most suitable for CDW deflection prediction, and can provide reasonable references for construction safety management on site. |
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| AbstractList | This paper proposes a convolution neural network (CNN) based prediction method for concrete diaphragm wall (CDW) deflections. CNN algorithm is modified for processing the CDW deformation data collected from in-situ measurement in both time and space dimensions, and capable of making dynamic prediction based on the extracted spatiotemporal features of wall deflection. The proposed method is validated through investigating a project of deep excavation in Suzhou, China. The predicted results show excellent agreement with field measurement and yield mean absolute errors of 0.86 mm and 1.55 mm for nowcasting and forecasting tasks, respectively. Three prevailing algorithms in time series prediction, namely, back propagation neural network, long short-term memory and autoregressive integrated moving average, are conducted for comparison. The results illustrate that the CNN outperforms the other algorithms in terms of accuracy and execution time. Therefore, the proposed CNN model is the most suitable for CDW deflection prediction, and can provide reasonable references for construction safety management on site. This paper proposes a convolution neural network (CNN) based prediction method for concrete diaphragm wall (CDW) deflections. CNN algorithm is modified for processing the CDW deformation data collected from in-situ measurement in both time and space dimensions, and capable of making dynamic prediction based on the extracted spatiotemporal features of wall deflection. The proposed method is validated through investigating a project of deep excavation in Suzhou, China. The predicted results show excellent agreement with field measurement and yield mean absolute errors of 0.86 mm and 1.55 mm for nowcasting and forecasting tasks, respectively. Three prevailing algorithms in time series prediction, namely, back propagation neural network, long short-term memory and autoregressive integrated moving average, are conducted for comparison. The results illustrate that the CNN outperforms the other algorithms in terms of accuracy and execution time. Therefore, the proposed CNN model is the most suitable for CDW deflection prediction, and can provide reasonable references for construction safety management on site. |
| Author | Zhao, Hua-jing Chen, Xiu-ming Du, Jiang-tao Liu, Wei Shi, Pei-xin |
| Author_xml | – sequence: 1 givenname: Hua-jing surname: Zhao fullname: Zhao, Hua-jing organization: School of Rail Transportation, Soochow University – sequence: 2 givenname: Wei orcidid: 0000-0002-1748-9688 surname: Liu fullname: Liu, Wei email: ggoulmmeng@suda.edu.cn organization: School of Rail Transportation, Soochow University – sequence: 3 givenname: Pei-xin surname: Shi fullname: Shi, Pei-xin organization: School of Rail Transportation, Soochow University – sequence: 4 givenname: Jiang-tao surname: Du fullname: Du, Jiang-tao organization: Guangzhou Metro Design and Research Institute Co., Ltd – sequence: 5 givenname: Xiu-ming surname: Chen fullname: Chen, Xiu-ming organization: Suzhou Rail Transit Group Co., Ltd |
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| Keywords | Diaphragm wall Deformation Deep excavation Deep learning prediction |
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| SubjectTerms | Algorithms Artificial neural networks Back propagation networks Complex Fluids and Microfluidics Construction accidents & safety Construction industry Convolution Deep learning Deflection Deformation Diaphragm wall Dimensions Engineering Excavation Feature extraction Foundations Geoengineering Geotechnical Engineering & Applied Earth Sciences Hydraulics Long short-term memory Machine learning Measurement Neural networks Occupational safety Predictions Research Paper Safety management Soft and Granular Matter Soil Science & Conservation Solid Mechanics |
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| Title | Spatiotemporal deep learning approach on estimation of diaphragm wall deformation induced by excavation |
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