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There has been increased interest in missing sensor data imputation, which isubiquitous in the field of structural health monitoring (SHM) due todiscontinuous sensing caused by sensor malfunction. To address this fundamentalissue, this paper presents an incremental Bayesian tensor learning method forreconstruction of spatiotemporal missing data in SHM and forecasting ofstructural response. In particular, a spatiotemporal tensor is firstconstructed followed by Bayesian tensor factorization that extracts latentfeatures for missing data imputation. To enable structural response forecastingbased on incomplete sensing data, the tensor decomposition is furtherintegrated with vector autoregression in an incremental learning scheme. Theperformance of the proposed approach is validated on continuous field-sensingdata (including strain and temperature records) of a concrete bridge, based onthe assumption that strain time histories are highly correlated to temperaturerecordings. The results indicate that the proposed probabilistic tensorlearning approach is accurate and robust even in the presence of large rates ofrandom missing, structured missing and their combination. The effect of rankselection on the imputation and prediction performance is also investigated.The results show that a better estimation accuracy can be achieved with ahigher rank for random missing whereas a lower rank for structured missing.

Incremental Bayesian tensor learning for structural monitoring data imputation and response forecasting