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

Abstract

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.

Graphical abstract

Keywords

mechanical inverse problem / displacement information completion / digital structural orderings / data-based method / genetic algorithm

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Acknowledgements

This work was supported by the National Natural Science Foundation of China (Grant Nos. 11991030, 11991031, and 11972205).

Competing interests

The authors declare that they have no competing interests.

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