Deep learning model of concrete dam deformation prediction based on CNN Article Swipe
Xi Wen
,
Jie Yang
,
Jintao Song
,
Xudong Qu
·
YOU?
·
· 2020
· Open Access
·
· DOI: https://doi.org/10.1088/1755-1315/580/1/012042
YOU?
·
· 2020
· Open Access
·
· DOI: https://doi.org/10.1088/1755-1315/580/1/012042
The concrete dam deformation prediction model is a key measure to predict the evolution of structural behavior and evaluate the safe service status. This paper uses open-source deep learning framework TensorFlow as the platform and uses the mature convolutional neural network technology in deep learning theory to establish the concrete dam deformation safety prediction model based on a deep learning. The application of engineering examples shows that the residual map, mean square error, and average percentage error are used as the model fitting and prediction accuracy evaluation standards. Compared with the shallow neural network model and the traditional Statistical model, the concrete dam deformation prediction model based on deep learning has higher prediction accuracy and more stable performance, providing a new method for concrete dam deformation monitoring.
Related Topics
Concepts
Deep learning
Convolutional neural network
Residual
Deformation (meteorology)
Artificial neural network
Computer science
Artificial intelligence
Mean squared error
Measure (data warehouse)
Machine learning
Geotechnical engineering
Structural engineering
Engineering
Data mining
Geology
Algorithm
Statistics
Mathematics
Oceanography
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1088/1755-1315/580/1/012042
- OA Status
- diamond
- Cited By
- 7
- References
- 4
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W3105707231
All OpenAlex metadata
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https://openalex.org/W3105707231Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1088/1755-1315/580/1/012042Digital Object Identifier
- Title
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Deep learning model of concrete dam deformation prediction based on CNNWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2020Year of publication
- Publication date
-
2020-10-01Full publication date if available
- Authors
-
Xi Wen, Jie Yang, Jintao Song, Xudong QuList of authors in order
- Landing page
-
https://doi.org/10.1088/1755-1315/580/1/012042Publisher landing page
- Open access
-
YesWhether a free full text is available
- OA status
-
diamondOpen access status per OpenAlex
- OA URL
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https://doi.org/10.1088/1755-1315/580/1/012042Direct OA link when available
- Concepts
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Deep learning, Convolutional neural network, Residual, Deformation (meteorology), Artificial neural network, Computer science, Artificial intelligence, Mean squared error, Measure (data warehouse), Machine learning, Geotechnical engineering, Structural engineering, Engineering, Data mining, Geology, Algorithm, Statistics, Mathematics, OceanographyTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
7Total citation count in OpenAlex
- Citations by year (recent)
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2023: 3, 2022: 2, 2021: 2Per-year citation counts (last 5 years)
- References (count)
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4Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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| publication_date | 2020-10-01 |
| publication_year | 2020 |
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