  Structural condition identification based on monitoring data is important for
automatic civil infrastructure asset management. Nevertheless, the monitoring
data is almost always insufficient, because the real-time monitoring data of a
structure only reflects a limited number of structural conditions, while the
number of possible structural conditions is infinite. With insufficient
monitoring data, the identification performance may significantly degrade. This
study aims to tackle this challenge by proposing a deep transfer learning (TL)
approach for structural condition identification. It effectively integrates
physics-based and data-driven methods, by generating various training data
based on the calibrated finite element (FE) model, pretraining a deep learning
(DL) network, and transferring its embedded knowledge to the real
monitoring/testing domain. Its performance is demonstrated in a challenging
case, vibration-based condition identification of steel frame structures with
bolted connection damage. The results show that even though the training data
are from a different domain and with different types of labels, intrinsic
physics can be learned through the pretraining process, and the TL results can
be clearly improved, with the identification accuracy increasing from 81.8% to
89.1%. The comparative studies show that SHMnet with three convolutional layers
stands out as the pretraining DL architecture, with 21.8% and 25.5% higher
identification accuracy values over the other two networks, VGGnet-16 and
ResNet-18. The findings of this study advance the potential application of the
proposed approach towards expert-level condition identification based on
limited real-world training data.
