Supervised learning approaches including kernel estimation, random forests, additive models, and deep learning are proposed to estimate conditional covariance matrices for removing multivariate environmental influences in SHM beyond standard response surface modeling.
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Removal of Multivariate Environmental Influences in Structural Health Monitoring through Conditional Covariances and Supervised Learning
Supervised learning approaches including kernel estimation, random forests, additive models, and deep learning are proposed to estimate conditional covariance matrices for removing multivariate environmental influences in SHM beyond standard response surface modeling.