  Automated structural damage diagnosis after earthquakes is important for
improving the efficiency of disaster response and rehabilitation. In
conventional data-driven frameworks which use machine learning or statistical
models, structural damage diagnosis models are often constructed using
supervised learning. The supervised learning requires historical structural
response data and corresponding damage states (i.e., labels) for each building
to learn the building-specific damage diagnosis model. However, in
post-earthquake scenarios, historical data with labels are often not available
for many buildings in the affected area. This makes it difficult to construct a
damage diagnosis model. Further, directly using the historical data from other
buildings to construct a damage diagnosis model for the target building would
lead to inaccurate results. This is because each building has unique physical
properties and thus unique data distribution. To this end, we introduce a new
framework to transfer the model learned from other buildings to diagnose
structural damage states in the target building without any labels. This
framework is based on an adversarial domain adaptation approach that extracts
domain-invariant feature representations of data from different buildings. The
feature extraction function is trained in an adversarial way, which ensures
that the extracted feature distributions are robust to changes in structures
while being predictive of the damage states. With the extracted
domain-invariant feature representations, the data distributions become
consistent across different buildings. We evaluate our framework on both
numerical simulation and field data collected from multiple building
structures, which outperforms the state-of-the-art benchmark methods.
