TY - JOUR
T1 - A data-based inverse problem-solving method for predicting structural orderings
AU - Li, Yiwen
AU - Chen, Jianlong
AU - Liu, Guangyan
AU - Liu, Zhanli
AU - Zhang, Kai
N1 - Publisher Copyright:
© Higher Education Press 2025.
PY - 2025/1
Y1 - 2025/1
N2 - 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.
AB - 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.
KW - data-based method
KW - digital structural orderings
KW - displacement information completion
KW - genetic algorithm
KW - mechanical inverse problem
UR - https://www.scopus.com/pages/publications/85207322885
U2 - 10.1007/s11709-024-1078-y
DO - 10.1007/s11709-024-1078-y
M3 - Article
AN - SCOPUS:85207322885
SN - 2095-2430
VL - 19
SP - 22
EP - 33
JO - Frontiers of Structural and Civil Engineering
JF - Frontiers of Structural and Civil Engineering
IS - 1
ER -