{"_buckets": {"deposit": "63887194-f868-469f-8887-bd9e71af6d12"}, "_deposit": {"created_by": 15, "id": "2000035", "owner": "15", "owners": [15], "owners_ext": {"displayname": "\u5317\u898b\u5de5\u696d\u5927\u5b66\u5b66\u8853\u6a5f\u95a2\u30ea\u30dd\u30b8\u30c8\u30ea\uff08KIT-R\uff09", "username": "kitir"}, "pid": {"revision_id": 0, "type": "depid", "value": "2000035"}, "status": "published"}, "_oai": {"id": "oai:kitami-it.repo.nii.ac.jp:02000035", "sets": ["1:87"]}, "control_number": "2000035", "item_3_biblio_info_186": {"attribute_name": "\u66f8\u8a8c\u60c5\u5831", "attribute_value_mlt": [{"bibliographicIssueDates": {"bibliographicIssueDate": "2019-09", "bibliographicIssueDateType": "Issued"}, "bibliographicIssueNumber": "9", "bibliographicPageEnd": "839", "bibliographicPageStart": "822", "bibliographicVolumeNumber": "34", "bibliographic_titles": [{"bibliographic_title": "Computer\u2010Aided Civil and Infrastructure Engineering", "bibliographic_titleLang": "en"}]}]}, "item_3_description_184": {"attribute_name": "\u6284\u9332", "attribute_value_mlt": [{"subitem_description": "Deep learning has ushered in many breakthroughs in vision\u2010based detection via convolutional neural networks (CNNs), but the vibration\u2010based structural damage detection by CNN remains being refined. Thus, this study proposes a simple one\u2010dimensional CNN that detects tiny local structural stiffness and mass changes, and validates the proposed CNN on actual structures. Three independent acceleration databases are established based on a T\u2010shaped steel beam, a short steel girder bridge (in test field), and a long steel girder bridge (in service). The raw acceleration data are not pre\u2010processed and are directly used as the training and validation data. The well\u2010trained CNN almost perfectly identifies the locations of small local changes in the structural mass and stiffness, demonstrating the high sensitivity of the proposed simple CNN to tiny structural state changes in actual structures. The convolutional kernels and outputs of the convolutional and max pooling layers are visualized and discussed as well.", "subitem_description_language": "en", "subitem_description_type": "Abstract"}]}, "item_3_publisher_212": {"attribute_name": "\u51fa\u7248\u8005", "attribute_value_mlt": [{"subitem_publisher": "Wiley"}]}, "item_3_relation_191": {"attribute_name": "DOI", "attribute_value_mlt": [{"subitem_relation_type_id": {"subitem_relation_type_id_text": "https://doi.org/10.1111/mice.12447", "subitem_relation_type_select": "DOI"}}]}, "item_3_rights_192": {"attribute_name": "\u6a29\u5229", "attribute_value_mlt": [{"subitem_rights": "This is the peer reviewed version of the following article: Zhang, Y, Miyamori, Y, Mikami, S, Saito, T. Vibration\u2010based structural state identification by a 1\u2010dimensional convolutional neural network. Comput Aided Civ Inf. 2019; 34: 822\u2013 839., which has been published in final form at https://doi.org/10.1111/mice.12447. 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