A novel approach for deterioration and damage identification in building structures based on Stockwell-Transform and deep convolutional neural network. Issue 2 (3rd April 2022)

• Record Type: Journal Article
• Authors: Gharehbaghi, Vahid Reza; Kalbkhani, Hashem; Noroozinejad Farsangi, Ehsan; Yang, T.Y.; Nguyen, Andy; Mirjalili, Seyedali; Málaga-Chuquitaype, Christian
• Is Part Of: Journal of structural integrity and maintenance. Volume 7:Issue 2(2022)
• Page Start: 136
• Page End: 150
• Publication Date: 2022-04-03
• Subjects: Deterioration -- damage -- Stockwell Transform -- convolutional neural networks -- deep learning -- CNN; Structural stability -- Periodicals; Safety factor in engineering -- Periodicals; Structural engineering -- Periodicals; 624.171
• DOI: 10.1080/24705314.2021.2018840
• Languages: English
• ISSNs: 2470-5314

Abstract

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.