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

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 -