Dataset-Based Damage Detection (DBDD) methods are effective for structures with geometric complexity, strong interaction effects, and operational variability, such as liquid-containing tanks with changing fluid levels. In many DBDD applications, accurate damage identification relies on high-fidelity numerical models and extensive simulations across numerous damage scenarios, which results in substantial computational cost. Although finite element modeling is well suited for these problems, fine-meshed models are computationally expensive, whereas coarse-meshed models can introduce errors in extracted dynamic features and reduce damage detection accuracy. This paper proposes an efficient DBDD framework that reduces computational demand while preserving detection reliability. A genetic algorithm is used to select a compact set of highly informative dynamic features that are extracted from a coarse-meshed finite element model and are suitable for training a machine-learning damage classification model. By combining a reduced-cost numerical model with an optimized feature set, the proposed approach decreases simulation time and database storage requirements while maintaining accurate damage prediction. The method is validated on a fluid-filled cylindrical tank under multiple fluid levels, demonstrating robust damage detection performance under simulated response variability (different FEM solvers, mesh sizes, ±5% material perturbations, and SNR 40–10 dB noise). These results indicate that the proposed framework is a practical and accurate solution for structural health monitoring of complex engineering systems.
Amanabadi et al. (Mon,) studied this question.