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Advances in dynamic load identification based on data-driven techniques.
- Source :
-
Engineering Applications of Artificial Intelligence . Nov2023:Part B, Vol. 126, pN.PAG-N.PAG. 1p. - Publication Year :
- 2023
-
Abstract
- Dynamic loads on engineering structures are often difficult to measure directly. Therefore, indirect identification methods based on dynamic responses are commonly used. However, this approach is often influenced by ill-conditioned matrices, noise interference, unknown structural and/or material parameters, and difficulty of constructing transfer functions when the traditional physics-based model is used. To address these issues, significant strides have been made in data-driven identification of dynamic loads, which are model-free and independent of structural characteristics. This paper tries to present a comprehensive review of dynamic load identification methods based on data-driven techniques, covering two aspects: load localization and load reconstruction. Features of the widely used data-driven techniques such as the geometric method, reference database method, machine learning methods including SVM-based methods and ANN-based methods and deep learning methods are discussed in detail. Additionally, this paper offers insight into the challenges and prospects of the data-driven techniques for dynamic load identification. The review aims to provide valuable insights for identifying dynamic loads in complex structures based on data-driven techniques and suggests future research directions. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 09521976
- Volume :
- 126
- Database :
- Academic Search Index
- Journal :
- Engineering Applications of Artificial Intelligence
- Publication Type :
- Academic Journal
- Accession number :
- 173435158
- Full Text :
- https://doi.org/10.1016/j.engappai.2023.106871