Offshore support and naval vessels operate in complex and hazardous environments facing the risk of impact from falling objects, collisions and projectiles. Accurate impact localization is essential to guarantee safety of the individuals, the environment and the asset.

This thesis explores the feasibility of impact localization on steel plates and stiffened panels by utilizing the information carried by the stress waves generated during impacts. These waves propagate along the surface of the structure as Guided Ultrasonic Waves (GUW). The inherent time reversibility and spatial reciprocity properties of the wave equations allow the use of Time Reversal (TR) process of the recorded wave signals to localize impacts.

The study combines experimental testing with an analytical framework. Small scale controlled impact experiments were performed in the Structures Laboratory at TU Delft while large scale tests were conducted onboard a Shoalbuster vessel at DAMEN Shipyards in Gorinchem, allowing the assessment of the scalability and robustness of the method. Acoustic Emissions (AE) were generated through Pencil Lead Breaks (PLBs) and instrumented hammer impacts. TR was implemented virtually in the frequency domain using an analytical propagation formulation that models dispersion and wave amplitude decay due to geometric spreading. The novelty of the present research lies in extending the analytical TR framework from plates to stiffened panels by removing the effect of stiffeners in back-propagation. This is achieved by introducing a scalar Transmission Coefficient (Tc) into the analytical model.

In the small scale experiments two configurations were tested, a plate and a stiffened plate with a stiffener located at the midspan, both measuring 400 x 400 mm². The average localization error for the plate ranged from 11 to 15 mm, while stiffened panel tests showed slightly higher errors in the order of 12 to 23 mm, depending on the impact type. Larger errors were observed for the instrumented hammer impacts. In the large scale tests, a 7500 x 2000 mm² area was monitored. Localization accuracy decreased due to increased structural complexity, including variable plate thickness, multiple stiffeners, and high acoustic noise from parallel steel work activity. A mean localization error of 662 mm was achieved, demonstrating the method’s scalability and potential for real world application.

These results confirm that TR of GUW is a feasible method for impact localization across different scales. The developed methodology shows potential for extension to composite materials and towards a complete impact identification framework that includes impact severity estimation, contributing to the development of integrated Structural Health Monitoring (SHM) systems capable of detecting, localizing, and quantifying structural impacts.

This thesis investigates the feasibility and effectiveness of Acoustic Emission (AE) methods for monitoring fatigue crack growth in metallic materials, with the aim of enhancing predictive capabilities and understanding of crack propagation under cyclic loading. The research specifically examines the correlation between various AE parameters—such as amplitude, count rate, energy rate, and entropy—and fatigue crack growth rates, using a multi-parametric approach.

Experiments were conducted on multiple specimens under different loading conditions, and both time-domain and frequency-domain AE parameters were analyzed. The study found that parameters like energy rate and rise angle were particularly effective in detecting specific stages of fatigue crack growth, while count rate and amplitude provided consistent indicators of crack initiation and progression. However, the study also highlighted limitations in the use of filtering techniques, such as SNR and amplitude filters, which can inadvertently remove crucial AE signals.

The findings suggest that while AE methods have potential for accurately monitoring fatigue crack growth, their effectiveness is influenced by the choice of AE parameters and the management of noise. To improve accuracy, the study recommends further research that includes a broader range of specimens, explores additional AE parameters, integrates complementary techniques such as Digital Image Correlation (DIC), and applies advanced analytical methods like machine learning. Future research should also consider the impact of environmental factors, such as corrosion fatigue, particularly in marine environments where realistic AE data is critical.

Overall, this study contributes to the broader understanding of AE monitoring for fatigue damage, laying a foundation for future research and practical applications, while acknowledging the need for further refinement and validation of AE techniques across diverse materials and conditions.