  In this paper, a novel deterioration and damage identification procedure
(DIP) is presented and applied to building models. The challenge associated
with applications on these types of structures is related to the strong
correlation of responses, which gets further complicated when coping with real
ambient vibrations with high levels of noise. Thus, a DIP is designed utilizing
low-cost ambient vibrations to analyze the acceleration responses using the
Stockwell transform (ST) to generate spectrograms. Subsequently, the ST outputs
become the input of two series of Convolutional Neural Networks (CNNs)
established for identifying deterioration and damage to the building models. To
the best of our knowledge, this is the first time that both damage and
deterioration are evaluated on building models through a combination of ST and
CNN with high accuracy.
