A data-based inverse problem-solving method for predicting structural orderings
Guardado en:
| Publicado en: | Frontiers of Structural and Civil Engineering vol. 19, no. 1 (Jan 2025), p. 22 |
|---|---|
| Autor principal: | |
| Otros Autores: | , , , |
| Publicado: |
Springer Nature B.V.
|
| Materias: | |
| Acceso en línea: | Citation/Abstract Full Text - PDF |
| Etiquetas: |
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
MARC
| LEADER | 00000nab a2200000uu 4500 | ||
|---|---|---|---|
| 001 | 3275181304 | ||
| 003 | UK-CbPIL | ||
| 022 | |a 2095-2430 | ||
| 022 | |a 2095-2449 | ||
| 022 | |a 1673-7407 | ||
| 024 | 7 | |a 10.1007/s11709-024-1078-y |2 doi | |
| 035 | |a 3275181304 | ||
| 045 | 2 | |b d20250101 |b d20250131 | |
| 084 | |a 108538 |2 nlm | ||
| 100 | 1 | |a Li, Yiwen |u Beijing Institute of Technology, School of Aerospace Engineering, Beijing, China (GRID:grid.43555.32) (ISNI:0000 0000 8841 6246) | |
| 245 | 1 | |a A data-based inverse problem-solving method for predicting structural orderings | |
| 260 | |b Springer Nature B.V. |c Jan 2025 | ||
| 513 | |a Journal Article | ||
| 520 | 3 | |a Inverse problem-solving methods have found applications in various fields, such as structural mechanics, acoustics, and non-destructive testing. However, accurately solving inverse problems becomes challenging when observed data are incomplete. Fortunately, advancements in computer science have paved the way for data-based methods, enabling the discovery of nonlinear relationships within diverse data sets. In this paper, a step-by-step completion method of displacement information is introduced and a data-driven approach for predicting structural parameters is proposed. The accuracy of the proposed approach is 23.83% higher than that of the Genetic Algorithm, demonstrating the outstanding accuracy and efficiency of the data-driven approach. This work establishes a framework for solving mechanical inverse problems by leveraging a data-based method, and proposes a promising avenue for extending the application of the data-driven approach to structural health monitoring. | |
| 653 | |a Problem solving | ||
| 653 | |a Structural health monitoring | ||
| 653 | |a Nondestructive testing | ||
| 653 | |a Genetic algorithms | ||
| 653 | |a Inverse problems | ||
| 653 | |a Acoustics | ||
| 653 | |a Accuracy | ||
| 653 | |a Data processing | ||
| 653 | |a Numerical analysis | ||
| 653 | |a Aerospace engineering | ||
| 653 | |a Mechanics | ||
| 653 | |a Boundary conditions | ||
| 653 | |a Efficiency | ||
| 653 | |a Machine learning | ||
| 653 | |a Partial differential equations | ||
| 653 | |a Sensors | ||
| 653 | |a Neural networks | ||
| 653 | |a Methods | ||
| 653 | |a Optimization algorithms | ||
| 653 | |a Environmental | ||
| 700 | 1 | |a Chen, Jianlong |u Beijing Institute of Technology, School of Aerospace Engineering, Beijing, China (GRID:grid.43555.32) (ISNI:0000 0000 8841 6246) | |
| 700 | 1 | |a Liu, Guangyan |u Beijing Institute of Technology, School of Aerospace Engineering, Beijing, China (GRID:grid.43555.32) (ISNI:0000 0000 8841 6246) | |
| 700 | 1 | |a Liu, Zhanli |u Tsinghua University, Applied Mechanics Laboratory, Department of Engineering Mechanics, School of Aerospace, Beijing, China (GRID:grid.12527.33) (ISNI:0000 0001 0662 3178) | |
| 700 | 1 | |a Zhang, Kai |u Beijing Institute of Technology, School of Aerospace Engineering, Beijing, China (GRID:grid.43555.32) (ISNI:0000 0000 8841 6246); Beijing Institute of Technology, Tangshan Research Institute, Tangshan, China (GRID:grid.43555.32) (ISNI:0000 0000 8841 6246) | |
| 773 | 0 | |t Frontiers of Structural and Civil Engineering |g vol. 19, no. 1 (Jan 2025), p. 22 | |
| 786 | 0 | |d ProQuest |t ABI/INFORM Global | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/3275181304/abstract/embedded/6A8EOT78XXH2IG52?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text - PDF |u https://www.proquest.com/docview/3275181304/fulltextPDF/embedded/6A8EOT78XXH2IG52?source=fedsrch |