A Novel Approach for Deterioration and Damage Identification in Building Structures Based on Stockwell-Transform and Deep Convolutional Neural Network
This addresses structural health monitoring for civil engineers, but it is incremental as it applies existing methods to a specific domain.
The paper tackled the problem of identifying deterioration and damage in building structures using low-cost ambient vibrations, achieving high accuracy by combining Stockwell transform and convolutional neural networks.
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.