Unbiased Normalized Ensemble Methodology for Zero-Shot Structural Damage Detection Using Manifold Learning and Reconstruction Error From Variational Autoencoder

Abstract

Zero-shot learning approaches have emerged as promising techniques for structural health monitoring (SHM) due to their ability to learn representations without labeled data. With the practical design of such models, the shift from traditional structure-dependent techniques to potentially large-scale implementations becomes feasible, effectively addressing the challenge of gathering labeled data. Autoencoders (AEs), a class of deep neural networks, align well with zero-shot SHM settings due to their architecture, loss function, and optimization process. In AEs, the reconstruction error is expected to increase for novel data patterns (i.e., potential damage data), while the encoded manifold in their bottleneck layers enables the discrimination of complex patterns. However, for practical SHM applications, rigorous evaluation of (variational) AEs and the robustness of reconstruction loss- or manifold-based designs in handling real-world scenarios remains necessary. Accordingly, this article employs two SHM benchmarks to evaluate the effectiveness of manifold learning compared to the reconstruction errors of (variational) AEs in a zero-shot setting. The comparison encompasses metrics such as reconstruction fidelity, preservation of structural characteristics, and the ability to generalize to unseen structural conditions. Furthermore, an unbiased normalization-based ensemble methodology is proposed, combining both approaches with the goal of enhancing damage detection performance and delivering more reliable results in zero-shot learning contexts. The proposed ensemble strategy, integrating both reconstruction error and manifold representations, adds robustness to the damage detection process, a crucial feature in the uncertain domain of zero-shot structural damage detection. The findings suggest that neither reconstruction loss nor manifold data consistently outperform the other; structural differences may render one approach more effective than the other in specific contexts, and based on these observations, a zero-shot damage severity index is suggested and tested on the benchmark data. Nevertheless, the proposed ensemble method demonstrates superior performance over individual models in estimating damage severity in an unsupervised setting. These results highlight the efficacy of variational AEs for zero-shot SHM, offering insights into their strengths and limitations and aiding users in selecting appropriate zero-shot damage detection strategies in the absence of labeled data.