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Lee, Young-Joo
Structural Reliability and Disaster Risk Lab.
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dc.citation.endPage 103 -
dc.citation.number 2 -
dc.citation.startPage 93 -
dc.citation.title SMART STRUCTURES AND SYSTEMS -
dc.citation.volume 33 -
dc.contributor.author Lee, Seungjun -
dc.contributor.author Lee, Jaebeom -
dc.contributor.author Kim, Minsun -
dc.contributor.author Lee, Sangmok -
dc.contributor.author Lee, Young-Joo -
dc.date.accessioned 2024-05-27T10:05:08Z -
dc.date.available 2024-05-27T10:05:08Z -
dc.date.created 2024-05-25 -
dc.date.issued 2024-02 -
dc.description.abstract Despite the rapid development of sensors, structural health monitoring (SHM) still faces challenges in monitoring due to the degradation of devices and harsh environmental loads. These challenges can lead to measurement errors, missing data, or outliers, which can affect the accuracy and reliability of SHM systems. To address this problem, this study proposes a classification method that detects anomaly patterns in sensor data. The proposed classification method involves several steps. First, data scaling is conducted to adjust the scale of the raw data, which may have different magnitudes and ranges. This step ensures that the data is on the same scale, facilitating the comparison of data across different sensors. Next, informative features in the time and frequency domains are extracted and used as input for a deep neural network model. The model can effectively detect the most probable anomaly pattern, allowing for the timely identification of potential issues. To demonstrate the effectiveness of the proposed method, it was applied to actual data obtained from a long -span cable -stayed bridge in China. The results of the study have successfully verified the proposed method's applicability to practical SHM systems for civil infrastructures. The method has the potential to significantly enhance the safety and reliability of civil infrastructures by detecting potential issues and anomalies at an early stage. -
dc.identifier.bibliographicCitation SMART STRUCTURES AND SYSTEMS, v.33, no.2, pp.93 - 103 -
dc.identifier.doi 10.12989/sss.2024.33.2.093 -
dc.identifier.issn 1738-1584 -
dc.identifier.scopusid 2-s2.0-85186959144 -
dc.identifier.uri https://scholarworks.unist.ac.kr/handle/201301/82706 -
dc.identifier.wosid 001221655100005 -
dc.language 영어 -
dc.publisher TECHNO-PRESS -
dc.title Deep learning-based anomaly detection in acceleration data of long-span cable-stayed bridges -
dc.type Article -
dc.description.isOpenAccess FALSE -
dc.relation.journalWebOfScienceCategory Engineering, Civil;Engineering, Mechanical;Instruments & Instrumentation -
dc.relation.journalResearchArea Engineering;Instruments & Instrumentation -
dc.type.docType Article -
dc.description.journalRegisteredClass scie -
dc.description.journalRegisteredClass scopus -
dc.subject.keywordAuthor anomaly patterns -
dc.subject.keywordAuthor deep neural network (DNN) -
dc.subject.keywordAuthor feature extraction -
dc.subject.keywordAuthor sensor data -
dc.subject.keywordAuthor structural health monitoring (SHM) -
dc.subject.keywordPlus SENSOR FAULT -
dc.subject.keywordPlus NETWORK -

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