Abstract
Structural health monitoring plays a significant role in civil engineering. To achieve more accurate prognostic evaluation of historic masonry structures, this paper presents a novel local linearization-based Gaussian process regression (LL-GPR) model. Local linearization is used to characterize the state of health (SOH) values of adjacent data points. Gaussian process regression is employed to predict the approximate linear relations. The proposed model is validated through a case study. The results demonstrate that the prediction results of LL-GPR can well reflect the degradation trend of SOH. Moreover, compared with the published methods, more accurate SOH prediction results can be obtained under this model
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