  Data-driven method for Structural Health Monitoring (SHM), that mine the
hidden structural performance from the correlations among monitored time series
data, has received widely concerns recently. However, missing data
significantly impacts the conduction of this method. Missing data is a
frequently encountered issue in time series data in SHM and many other
real-world applications, that harms to the standardized data mining and
downstream tasks, such as condition assessment. Imputation approaches based on
spatiotemporal relations among monitoring data are developed to handle this
issue, however, no additional information is added during imputation. This
paper thus develops a robust method for damage identification that considers
the missing data occasions, based on long-short term memory (LSTM) model and
dropout mechanism in the autoencoder (AE) framework. Inputs channels are
randomly dropped to simulate the missing data in training, and reconstruction
errors are used as the loss function and the damage indicator. Quasi-static
response (cable tension) of a cable-stayed bridge released in 1st IPC-SHM is
employed to verify this proposed method, and results show that the missing data
imputation and damage identification can be implemented together in a unified
way.
