Conference Agenda

The performance of strain-based Structural Health Monitoring (SHM) systems is highly dependent on the spatial distribution and quality of the sensing network. Inadequate sensor placement can reduce damage sensitivity, increase prediction uncertainty, and compromise the accurate localization of structural defects. This work presents a sensor positioning optimization framework for crack detection in metallic structures using large-scale virtual strain datasets generated via Extended Finite Element Method (XFEM) simulations. A comprehensive database comprising thousands of crack scenarios—varying in position, length, and orientation—is constructed to capture the structural response under diverse damage conditions. The numerical models are previously validated against experimental strain-gauge measurements obtained from fatigue tests on stiffened panels, ensuring realistic representation of strain fields.
Sensor placement is optimized through a combination of strain-based sensitivity metrics and information-theoretic criteria. Strain Difference Coverage (SDC) and Strain Contribution Coverage (SCC) are employed to quantify, respectively, the variability of strain responses across damage scenarios and the relative contribution of each location to the global structural response. In parallel, the Fisher Information Matrix (FIM) is used to evaluate the parameter observability associated with candidate sensor configurations, enabling the identification of layouts that minimize estimation uncertainty.
The proposed framework integrates these complementary approaches to systematically identify sensor locations that maximize damage detectability while reducing the number of required sensors. Results demonstrate that optimized sensor configurations significantly outperform uniform distributions, particularly when considering full crack characterization (location, length, and orientation). Additionally, consistent patterns observed across different techniques highlight the importance of combining optimization methods with engineering judgment to capture physically meaningful sensing regions. The methodology provides a robust, interpretable, and computationally efficient approach for sensor placement optimization in structural SHM applications.

Optimal Sensor Placement (OSP) is a critical component of vibration-based Structural Health Monitoring (SHM), yet its effectiveness is often compromised by the deterministic nature of standard Finite Element (FE) models. This study evaluates two advanced OSP frameworks that explicitly account for epistemic and aleatory uncertainties: a variance-based method that utilizes hierarchical clustering to manage modal sensitivity variance, and a likelihood-maximization method designed to maximize the probability of achieving specific SHM objectives under measurement noise. Using a laboratory-tested glulam timber beam as a benchmark, the research investigates the impact of uncertain material properties and support stiffness on sensor performance. Experimental results from ambient and impact vibration tests serve as the ground truth for validation. The findings reveal that boundary condition uncertainties can lead to significant modal discrepancies, especially for higher modes. Both probabilistic methods identified robust sensor configurations that significantly outperformed random layouts, with the likelihood-maximization method achieving a 65.9% success rate for high-fidelity mode shape reconstruction (MAC ≥ 0.95). By bridging the gap between theoretical optimization and practical structural variability, these frameworks provide a reliable methodology for designing SHM systems in complex, real-world infrastructure.

In a global context driven by energy transition and the imperative to reduce dependence on fossil resources, the development of safe and high-performance hydrogen economy has emerge as a strategic priority for industry. Hydrogen tanks, critical components in the transport and energy sectors, must ensure high reliability throughout their service life. However, ensuring their structural integrity remains a major challenge both during manufacturing and operation, due to damage mechanisms that may be difficult to detect and potentially detrimental to safety. In this context, identifying and qualifying advanced Structural Health Monitoring (SHM) technologies is a key industrial objective.

This study investigates several innovative SHM solutions applied to materials intended for composite hydrogen storage vessels. Two main families of sensors are evaluated: (i) distributed optical fibers capable of providing continuous strain measurements over the full sensing length or specific regions, and (ii) flexible piezoelectric sensors based on P(VDF-TrFE) copolymers, manufactured by screen printing, enabling easy integration into composite structures. These emerging technologies are compared with more conventional devices such as resistive strain gauges and ceramic acoustic emission sensors, in order to assess their performance and sensitivity to damage phenomena.

The experimental work is conducted on two types of standardized specimens: a classical rectangular geometry and a tubular geometry representative of filament-wound tank structures. Some specimens are intentionally pre-damaged through low-energy impact to generate Barely Visible Impact Damage (BVID), a type of defect frequently encountered in composites. All samples are then subjected to quasi-static mechanical tests: monotonic tensile loading for rectangular specimens and NOL-ring (Naval Ordonance Laboratories) tensile tests for tubular specimens, thus approaching conditions reflecting in-service loads.

The results demonstrate strong consistency among the different measurement techniques, both in detecting critical events and in tracking strain evolution. Distributed optical fibers show high capability in accurately localizing stressed areas, while the printed piezoelectric sensors effectively capture damage-related signals, although with a lower event count compared to ceramic sensors. These findings confirm the relevance of the proposed SHM approaches and highlight their potential for future integration into hydrogen storage vessels to enhance in-service monitoring and structural safety.