V. Gharehbaghi, H. Kalbkhani, E. N. Farsangi, Tony T. Y. Yang, A. Nguyen, S. Mirjalili, C. Málaga‐Chuquitaype
{"title":"基于stockwell变换和深度卷积神经网络的建筑结构退化与损伤识别新方法","authors":"V. Gharehbaghi, H. Kalbkhani, E. N. Farsangi, Tony T. Y. Yang, A. Nguyen, S. Mirjalili, C. Málaga‐Chuquitaype","doi":"10.1080/24705314.2021.2018840","DOIUrl":null,"url":null,"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.","PeriodicalId":43844,"journal":{"name":"Journal of Structural Integrity and Maintenance","volume":"7 1","pages":"136 - 150"},"PeriodicalIF":3.0000,"publicationDate":"2021-11-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"6","resultStr":"{\"title\":\"A novel approach for deterioration and damage identification in building structures based on Stockwell-Transform and deep convolutional neural network\",\"authors\":\"V. Gharehbaghi, H. Kalbkhani, E. N. Farsangi, Tony T. Y. Yang, A. Nguyen, S. Mirjalili, C. Málaga‐Chuquitaype\",\"doi\":\"10.1080/24705314.2021.2018840\",\"DOIUrl\":null,\"url\":null,\"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.\",\"PeriodicalId\":43844,\"journal\":{\"name\":\"Journal of Structural Integrity and Maintenance\",\"volume\":\"7 1\",\"pages\":\"136 - 150\"},\"PeriodicalIF\":3.0000,\"publicationDate\":\"2021-11-11\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"6\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of Structural Integrity and Maintenance\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1080/24705314.2021.2018840\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"ENGINEERING, CIVIL\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Structural Integrity and Maintenance","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1080/24705314.2021.2018840","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"ENGINEERING, CIVIL","Score":null,"Total":0}
A novel approach for deterioration and damage identification in building structures based on Stockwell-Transform and deep convolutional neural network
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.