The stochastic Volterra series method, while powerful for damage detection, does have limitations regarding computational cost and scalability, particularly when applied to larger and more complex structures. One significant limitation is the computational burden associated with estimating the Volterra kernels, especially as the order of the series increases. The need for Monte Carlo simulations to achieve convergence in the estimation of the stochastic model can lead to high computational demands, requiring substantial processing time and resources.
As the complexity of the structure increases, the number of parameters in the Volterra series also increases, which can lead to overfitting if not managed properly. This complexity can make it challenging to obtain reliable estimates of the Volterra kernels, particularly in real-time applications where quick decision-making is crucial.
Additionally, the method's scalability is constrained by the need for extensive experimental data to accurately characterize the system's behavior under various conditions. For large structures, collecting sufficient data can be logistically challenging and time-consuming. Furthermore, the stochastic nature of the model may require more sophisticated data processing techniques to handle the increased variability in measurements, which can further complicate the analysis.