Finite element model updating for structural damage detection using transmissibility data

• Published in: Earthquake Engineering and Engineering Vibration Vol. 23; no. 1; pp. 87 - 101
• Main Authors: Izadi, Ahmad, Esfandiari, Akbar
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer Berlin Heidelberg 01.01.2024; Springer Nature B.V; Department of Maritime Engineering,Amirkabir University of Technology,Tehran 15875-4413,Iran
• Subjects: Algorithms; Civil Engineering; Control; Damage assessment; Damage detection; Dynamical Systems; Earth and Environmental Science; Earth Sciences; Finite element method; Frequency dependence; Frequency response functions; Geotechnical Engineering & Applied Earth Sciences; Mathematical models; Model updating; Parameter estimation; Parameters; Sensitivity; Structural damage; Structural response; Technical Papers; Vibration; damage detection; model updating; output-only; sensitivity equation; transmissibility
• ISSN: 1671-3664; 1993-503X
• DOI: 10.1007/s11803-024-2229-9

This paper presents a new finite element model updating method for estimating structural parameters and detecting structural damage location and severity based on the structural responses (output-only data). The method uses the sensitivity relation of transmissibility data through a least-squares algorithm and appropriate normalization of the extracted equations. The proposed transmissibility-based sensitivity equation produces a more significant number of equations than the sensitivity equations based on the frequency response function (FRF), which can estimate the structural parameters with higher accuracy. The abilities of the proposed method are assessed by using numerical data of a two-story two-bay frame model and a plate structure model. In evaluating different damage cases, the number, location, and stiffness reduction of the damaged elements and the severity of the simulated damage have been accurately identified. The reliability and stability of the presented method against measurement and modeling errors are examined using error-contaminated data. The parameter estimation results prove the method’s capabilities as an accurate model updating algorithm.