Response Prediction for Linear and Nonlinear Structures Based on Data-Driven Deep Learning Article Swipe
Yangyang Liao
,
Hesheng Tang
,
Rongshuai Li
,
Lingxiao Ran
,
Liyu Xie
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.3390/app13105918
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.3390/app13105918
Dynamic analysis of structures is very important for structural design and health monitoring. Conventional numerical or experimental methods often suffer from the great challenges of analyzing the responses of linear and nonlinear structures, such as high cost, poor accuracy, and low efficiency. In this study, the recurrent neural network (RNN) and long short-term memory (LSTM) models were used to predict the responses of structures with or without nonlinear components. The time series k-means (TSkmeans) algorithm was used to divide label data into different clusters to enhance the generalization of the models. The models were trained with different cluster acceleration records and the corresponding structural responses obtained by numerical methods, and then predicted the responses of nonlinear and linear structures under different seismic waves. The results showed that the two deep learning models had a good ability to predict the time history response of a linear system. The RNN and LSTM models could roughly predict the response trend of nonlinear structures, but the RNN model could not reproduce the response details of nonlinear structures (high-frequency characteristics and peak values).
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3390/app13105918
- https://www.mdpi.com/2076-3417/13/10/5918/pdf?version=1683795493
- OA Status
- gold
- Cited By
- 15
- References
- 39
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4376137892
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4376137892Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.3390/app13105918Digital Object Identifier
- Title
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Response Prediction for Linear and Nonlinear Structures Based on Data-Driven Deep LearningWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2023Year of publication
- Publication date
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2023-05-11Full publication date if available
- Authors
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Yangyang Liao, Hesheng Tang, Rongshuai Li, Lingxiao Ran, Liyu XieList of authors in order
- Landing page
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https://doi.org/10.3390/app13105918Publisher landing page
- PDF URL
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https://www.mdpi.com/2076-3417/13/10/5918/pdf?version=1683795493Direct link to full text PDF
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YesWhether a free full text is available
- OA status
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goldOpen access status per OpenAlex
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https://www.mdpi.com/2076-3417/13/10/5918/pdf?version=1683795493Direct OA link when available
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Nonlinear system, Generalization, Computer science, Recurrent neural network, Artificial intelligence, Artificial neural network, Series (stratigraphy), Algorithm, Linear model, Deep learning, Machine learning, Mathematics, Geology, Paleontology, Physics, Mathematical analysis, Quantum mechanicsTop concepts (fields/topics) attached by OpenAlex
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15Total citation count in OpenAlex
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2025: 9, 2024: 6Per-year citation counts (last 5 years)
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10Other works algorithmically related by OpenAlex
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