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A data-based inverse problem-solving method for predicting structural orderings

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Publicado en:Frontiers of Structural and Civil Engineering vol. 19, no. 1 (Jan 2025), p. 22
Autor Principal: Li, Yiwen
Outros autores: Chen, Jianlong, Liu, Guangyan, Liu, Zhanli, Zhang, Kai
Publicado:
Springer Nature B.V.
Materias:
Problem solving
Structural health monitoring
Nondestructive testing
Genetic algorithms
Inverse problems
Acoustics
Accuracy
Data processing
Numerical analysis
Aerospace engineering
Mechanics
Boundary conditions
Efficiency
Machine learning
Partial differential equations
Sensors
Neural networks
Methods
Optimization algorithms
Environmental
Acceso en liña:Citation/Abstract
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Resumo: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.
ISSN:2095-2430
2095-2449
1673-7407
DOI:10.1007/s11709-024-1078-y
Fonte:ABI/INFORM Global