A novel approach for deterioration and damage identification in building structures based on Stockwell-Transform and deep convolutional neural network

Vahid Reza Gharehbaghi, Hashem Kalbkhani, Ehsan Noroozinejad Farsangi, T. Y. Yang, Andy Nguyen, Seyedali Mirjalili, Christian Málaga-Chuquitaype

Research output: Contribution to journalArticlepeer-review

1 Citation (Scopus)

Abstract

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, an issue that 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 on 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.

Original languageEnglish
Pages (from-to)136-150
Number of pages15
JournalJournal of Structural Integrity and Maintenance
Volume7
Issue number2
DOIs
Publication statusPublished - 2022

Keywords

  • CNN
  • convolutional neural networks
  • damage
  • deep learning
  • Deterioration
  • Stockwell Transform

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