Domain-adaptive nonlinear probabilistic latent variable model for vibration-based structural damage detection under climate change

A major challenge in vibration-based structural damage detection is differentiating actual damage from environmental and operational variations. Most existing studies are limited to short-term scenarios and do not consider long-term variations. With climate change driving more frequent and intense extreme weather events, long-term structural monitoring data exhibit covariate shift, for example, annual mean temperature increases over time. Such shifts pose fundamental challenges for machine learning models trained on historical datasets, as traditional algorithms often fail to extrapolate to these out-of-distribution environmental domains. This study develops a domain-adaptive nonlinear probabilistic latent variable model to advance long-term damage detection under climate change. First, Bayesian inference and kernel techniques are integrated to capture underlying nonlinear environmental effects without explicit environmental data. Second, domain adaptation is incorporated to enhance model adaptability to covariate shift scenarios due to climate change. The developed method is applied to a laboratory-tested slab and the practical Z24 bridge. Structural vibration properties are estimated under changing temperature and humidity conditions with consideration of four climate emission scenarios. The results demonstrate that the proposed method can distinguish damage-induced variations from climate-related environmental effects. It outperforms traditional methods that may result in false-positive and false-negative damage detections due to climate change and environmental nonlinear impacts.