The insights gained from this study regarding the importance of accounting for uncertainties in damage detection can be significantly beneficial when applied to other structural health monitoring (SHM) techniques, such as vibration-based methods and guided wave-based methods.
In vibration-based methods, the recognition that uncertainties—such as environmental factors, measurement noise, and variations in boundary conditions—can obscure the true structural response is crucial. By integrating probabilistic models similar to the stochastic Volterra series, vibration-based methods can enhance their robustness against these uncertainties. This could involve developing confidence intervals for damage indices derived from modal parameters, allowing for a more reliable assessment of structural health.
For guided wave-based methods, which are often used for detecting damage in composite materials, the insights from this study can lead to improved signal processing techniques that account for uncertainties in wave propagation. By employing statistical methods to analyze the guided wave signals, practitioners can better differentiate between damage-related changes and variations caused by environmental factors or measurement errors.
Furthermore, the application of machine learning algorithms to both vibration-based and guided wave-based methods can be informed by the stochastic approach, enabling these techniques to learn from historical data and improve their predictive capabilities in the presence of uncertainties. This holistic approach to SHM can lead to more accurate and reliable damage detection across various structural types and conditions.