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In this model, the wind\u2010induced responses are estimated by CNN trained with previously measured sensor signals; this enables the SHM system to operate stably even when a sensor fault or data loss occurs. In the presented model, top\u2010level wind\u2010induced displacement in the time and frequency domains, and wind data in the frequency domain are configured into the input map of the CNN to reflect the resisting capacity of a tall building, the change in the dynamic characteristics of the building due to wind loads, and the relationship between wind load and the building. To evaluate stress, which is used as a safety indicator for structural members in the building, the maximum and minimum strains of columns are set as the output layer of the CNN. The CNN is trained using measured wind and wind response data to predict the column strains during a future wind load. The presented model is validated using data from a wind tunnel test of a building model. The performance of the presented model is verified through strain estimation with data that were not used in the CNN training. To assess the validity of the presented input map configuration, the estimation performance is compared with a CNN that considered only the time domain responses as input. Furthermore, the effects of the variations in the configuration of the CNN on the wind response estimation performance are examined.<\/jats:p>","DOI":"10.1111\/mice.12476","type":"journal-article","created":{"date-parts":[[2019,6,26]],"date-time":"2019-06-26T05:11:10Z","timestamp":1561525870000},"page":"843-858","update-policy":"http:\/\/dx.doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":71,"title":["Convolutional neural network\u2010based wind\u2010induced response estimation model for tall buildings"],"prefix":"10.1111","volume":"34","author":[{"given":"Byung Kwan","family":"Oh","sequence":"first","affiliation":[{"name":"Department of Civil and Environmental Engineering Princeton University Princeton NJ USA"}]},{"given":"Branko","family":"Glisic","sequence":"additional","affiliation":[{"name":"Department of Civil and Environmental Engineering Princeton University Princeton NJ USA"}]},{"given":"Yousok","family":"Kim","sequence":"additional","affiliation":[{"name":"Department of Architectural Engineering Hongik University Sejong Korea"}]},{"given":"Hyo Seon","family":"Park","sequence":"additional","affiliation":[{"name":"Department of Architectural Engineering Yonsei University Seoul Korea"}]}],"member":"311","published-online":{"date-parts":[[2019,6,25]]},"reference":[{"key":"e_1_2_6_2_1","doi-asserted-by":"publisher","DOI":"10.1088\/0964-1726\/24\/6\/065034"},{"key":"e_1_2_6_3_1","doi-asserted-by":"publisher","DOI":"10.1007\/s11831-014-9135-7"},{"key":"e_1_2_6_4_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.engstruct.2017.05.054"},{"key":"e_1_2_6_5_1","doi-asserted-by":"publisher","DOI":"10.1007\/s10618-016-0483-9"},{"key":"e_1_2_6_6_1","doi-asserted-by":"publisher","DOI":"10.1002\/stc.85"},{"key":"e_1_2_6_7_1","unstructured":"Borovykh A. 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