Timely monitoring the large-scale civil structure is a tedious task demanding expert experience and significant economic resources. Towards a smart monitoring system, this study proposes a hybrid deep learning algorithm aiming for structural damage detection tasks, which not only reduces required resources, including computational complexity, data storage but also has the capability to deal with different damage levels. The technique combines the ability to capture local connectivity of Convolution Neural Network and the well-known performance in accounting for long-term dependencies of Long-Short Term Memory network, into a single end-to-end architecture using directly raw acceleration time-series without requiring any signal preprocessing step. |
