This is our first publication on transfer learning for structural health monitoring of bridges. I do believe it embeds a new mind set for SHM of bridges.
This paper proposes an unsupervised transfer learning approach for bridges with a domain adaptation technique, where classifiers are trained only with labeled data generated from FE models (source domain). Then, unlabeled monitoring data (target domain) are used to test the classification performance. The domain adaptation is performed using a transfer knowledge method called transfer component analysis, which transforms damage-sensitive features from the original space to a new one, called latent space, where the differences between feature distributions are reduced. The efficiency of this unsupervised approach is illustrated through the classification performance of classifiers built on source data with and without domain adaptation, and using the benchmark data sets from the Z-24 Bridge as the target data.
For more information: https://ascelibrary.org/doi/abs/10.1061/(ASCE)BE.1943-5592.0001979.