Conference Agenda

This study addresses robust damage detection under uncertainty by combining the inverse Finite Element Method (iFEM) with Statistical Time Series (STS) concepts and the Multiple Model (MM) robust framework. The work extends a previously developed iFEM-based approach, demonstrated under ideal conditions, to cases involving uncertain and non-measurable EOCs. The proposed methodology exploits iFEM for full-field strain, deformation, and stress reconstruction from limited sensor measurements, while STS models and functionally pooled MM representations are used to model random vibration responses and account for uncertainty. The approach is assessed through numerical Monte Carlo experiments on a composite wing plate modelled with shell elements and subjected to random excitation over a temperature range from −20 °C to 20 °C in 4 °C steps. Early/minor damage scenarios and a limited number of sensors are considered. The results show improved damage detection performance compared with alternative methods indicating the practical potential of the combined iFEM–STS framework.

Reliable reconstruction of full-field displacements in damaged composites from sparse sensor measurements is a critical step toward accurate structural health monitoring of composite structures widely used across industries. This study experimentally evaluates the Particle Inverse Method (PIM), a novel particle-based approach for full-field deformation reconstruction from discrete strain measurements, on carbon fiber/epoxy plates containing centrally introduced vertical cracks. The PIM is inspired by the formulation of the inverse Finite Element Method (iFEM), yet it offers a broader framework capable of accurately reconstructing deformation fields even in the presence of damage evolution and crack propagation, where conventional iFEM formulations are limited. To demostrate the experimental capability of the proposed PIM approach, specimens are manufactured in three configurations comprising 0° unidirectional (UD), 90° UD, and 0/90° cross-ply lay-ups. Discrete strain sensors, strain gauges and Fiber Bragg Gratings, are mounted on one face, and incremental tensile loading is applied using a universal testing machine, while Digital Image Correlation (DIC) concurrently records full-field displacements on the opposite face. The discrete strain measurements serve as inputs to PIM, which reconstructs the displacement fields at each load state without prior knowledge of material properties or applied load magnitudes. The reconstructed fields are quantitatively compared with DIC measurements to assess PIM’s accuracy across damaged lay-up configurations.

Results indicate that PIM reliably reconstructs full-field displacements in cracked composite plates, capturing both global deformation patterns and local discontinuities induced by the crack. Agreement with DIC persists across fiber orientations, highlighting PIM’s ability to handle orthotropy and damage-driven localization from sparse sensing. The method’s independence from a priori material and load characterization reduces test-specific calibration effort and broadens applicability to settings where only limited sensor data is available. In addition, PIM’s displacement reconstruction workflow is compatible with real-time deployment, enabling continuous shape sensing during operation and facilitating timely detection and tracking of damage evolution. Collectively, these findings support PIM as a practical sensing tool for robust monitoring of composite components under service conditions and establish a validated pathway to integrate sparse strain sensing with high-fidelity, full-field kinematic information for decision-ready structural health assessment.

Shape sensing, which can real-time monitor the dynamics deformation of the engineering structure using the discrete surface strains, has a significance for structural health monitoring system (SHM). Nevertheless, the current shape sensing approaches is limited for monitoring the three-dimensional nonlinear displacements and the reconstructed transverse displacement affects the accuracy of the axial displacement reconstruction. To estimate the structural deformation information though discrete strain measures, a nonlinear shape sensing approach is proposed to monitor the large deformation based on the Green-Lagrange strain gradient theory. In this methodology, the nonlinear strain functions are linearized and the iterative shape sensing model is established, where the inverse finite element method (iFEM ) is used to calculate the initial displacements and update the analogy stiffness matrix and analogy load vector. Several case studies are presented to demonstrate the accuracy and stability of the proposed nonlinear iFEM approach. It is proven that the proposed method can improve the estimation accuracy by up to 5.10% with respect to the linear iFEM methodology. Hence, the proposed strategy can be employed as a viable tool to predict the dynamic displacement responses of smart engineering and then the structural stability can be real-time monitored in service.