A novel delamination damage detection strategy based on inverse finite element method for structural health monitoring of composite structures. (1st June 2023)

• Record Type: Journal Article
• Authors: Ganjdoust, F.; Kefal, A.; Tessler, A.
• Is Part Of: Mechanical systems and signal processing. Volume 192(2023)
• Publication Date: 2023-06-01
• Subjects: Damage detection -- Laminated composite shells -- Inverse finite element method -- Refined zigzag theory -- Structural health monitoring; Structural dynamics -- Periodicals; Vibration -- Periodicals; Constructions -- Dynamique -- Périodiques; Vibration -- Périodiques; Structural dynamics; Vibration; Periodicals; 621
• DOI: 10.1016/j.ymssp.2023.110202
• Languages: English
• ISSNs: 0888-3270

Abstract

Abstract: In recent years, structural health monitoring (SHM) has been revolutionized with the advent of the inverse finite element method (iFEM), which is a superior sensing technology based on the minimization of a weighted least squares error functional between experimental and numerical strain measures. This approach is suitable for damage detection thanks to its highly accurate and full-field displacement reconstruction capability within the physical domain of the structure. This study focuses on the development of a novel damage detection strategy for identifying internal/external defect types in composites, e.g., delamination, surface debonding, etc., by utilizing iFEM. The core formulation is derived by employing the kinematic relations of the refined zigzag theory (RZT) within the iFEM framework. By utilizing the field variables achieved via the iFEM-RZT, equivalent von Mises strains are computed for individual plies. After that, through the definition of various damage indices, the health of the structure is evaluated in terms of the presence of damage as well as its extent and through-the-thickness position and in-plane size of the damage in laminated composite materials. Various case studies with different damage scenarios are simulated for the assessment of iFEM-RZT capability in terms of shape-sensing and SHM. As a result, the inverse algorithm shows its remarkable efficiency and accuracy in detecting flawed regions over the problem domain and through the thickness of layered materials, both in terms of the location of the damage as well as its morphology.