  In modern building infrastructures, the chance to devise adaptive and
unsupervised data-driven health monitoring systems is gaining in popularity due
to the large availability of big data from low-cost sensors with communication
capabilities and advanced modeling tools such as Deep Learning. The main
purpose of this paper is to combine deep neural networks with Bidirectional
Long Short Term Memory and advanced statistical analysis involving
Instantaneous Frequency and Spectral Kurtosis to develop an accurate
classification tool for tensile, shear and mixed modes originated from acoustic
emission events (cracks). We investigated on effective event descriptors to
capture the unique characteristics from the different types of modes. Tests on
experimental results confirm that this method achieves promising classification
among different crack events and can impact on the design of future on
structural health monitoring (SHM) technologies. This approach is effective to
classify incipient damages with 92% of accuracy, which is advantageous to plan
maintenance.
