  Structural damage detection has become an interdisciplinary area of interest
for various engineering fields, while the available damage detection methods
are being in the process of adapting machine learning concepts. Most machine
learning based methods heavily depend on extracted ``hand-crafted" features
that are manually selected in advance by domain experts and then, fixed.
Recently, deep learning has demonstrated remarkable performance on traditional
challenging tasks, such as image classification, object detection, etc., due to
the powerful feature learning capabilities. This breakthrough has inspired
researchers to explore deep learning techniques for structural damage detection
problems. However, existing methods have considered either spatial relation
(e.g., using convolutional neural network (CNN)) or temporal relation (e.g.,
using long short term memory network (LSTM)) only. In this work, we propose a
novel Hierarchical CNN and Gated recurrent unit (GRU) framework to model both
spatial and temporal relations, termed as HCG, for structural damage detection.
Specifically, CNN is utilized to model the spatial relations and the short-term
temporal dependencies among sensors, while the output features of CNN are fed
into the GRU to learn the long-term temporal dependencies jointly. Extensive
experiments on IASC-ASCE structural health monitoring benchmark and scale model
of three-span continuous rigid frame bridge structure datasets have shown that
our proposed HCG outperforms other existing methods for structural damage
detection significantly.
