Bayesian-Optimized Multi-Source Domain Adaptation for Post-Earthquake Damage Assessment

Abstract

Efficient and intelligent post-earthquake structural damage assessment is critical for rapid disaster response. Although data-driven approaches have shown promise in this domain, traditional supervised learning relies on large labeled datasets that are impractical to obtain for earthquake-damaged structures. To overcome this limitation, we propose a Bayesian-optimized multisource domain adaptation framework for predicting post-earthquake structural damage on a target building without the need for any damage labels. The framework comprises three key steps. First, it extracts features from multiple source domains and the target domain and feeds them into a classifier and a domain discriminator. The classifier ensures the features remain damage-sensitive, while the discriminator promotes their invariance across domains. Second, the framework assigns a weighing factor to each source domain to balance their contributions during training. Finally, Bayesian optimization is employed to optimize these source domain weights, aiming to maximize prediction accuracy on the target domain. This framework offers a robust solution for structural damage assessment when labeled data are scarce, significantly enhancing post-earthquake damage assessment capabilities.