{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,3]],"date-time":"2026-06-03T03:44:13Z","timestamp":1780458253728,"version":"3.54.1"},"reference-count":79,"publisher":"MDPI AG","issue":"7","license":[{"start":{"date-parts":[[2021,4,3]],"date-time":"2021-04-03T00:00:00Z","timestamp":1617408000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100003725","name":"National Research Foundation of Korea","doi-asserted-by":"publisher","award":["NRF-2018R1A5A1025137"],"award-info":[{"award-number":["NRF-2018R1A5A1025137"]}],"id":[{"id":"10.13039\/501100003725","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Wind tunnel testing techniques are the main research tools for evaluating the wind loadings of buildings. They are significant in designing structurally safe and comfortable buildings. The wind tunnel pressure measurement technique using pressure sensors is significant for assessing the cladding pressures of buildings. However, some pressure sensors usually fail and cause loss of data, which are difficult to restore. In the literature, numerous techniques are implemented for imputing the single instance data values and data imputation for multiple instantaneous time intervals with accurate predictions needs to be addressed. Thus, the data imputation capacity of machine learning models is used to predict the missing wind pressure data for tall buildings in this study. A generative adversarial imputation network (GAIN) is proposed to predict the pressure coefficients at various instantaneous time intervals on tall buildings. The proposed model is validated by comparing the performance of GAIN with that of the K-nearest neighbor and multiple imputations by chained equation models. The experimental results show that the GAIN model provides the best fit, achieving more accurate predictions with the minimum average variance and minimum average standard deviation. The average mean-squared error for all four sides of the building was the minimum (0.016), and the average R-squared error was the maximum (0.961). The proposed model can ensure the health and prolonged existence of a structure based on wind environment.<\/jats:p>","DOI":"10.3390\/s21072515","type":"journal-article","created":{"date-parts":[[2021,4,3]],"date-time":"2021-04-03T22:03:36Z","timestamp":1617487416000},"page":"2515","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":22,"title":["Wind-Induced Pressure Prediction on Tall Buildings Using Generative Adversarial Imputation Network"],"prefix":"10.3390","volume":"21","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4226-7435","authenticated-orcid":false,"given":"Bubryur","family":"Kim","sequence":"first","affiliation":[{"name":"Department of Architectural Engineering, Dong-A University, Busan 49315, Korea"},{"name":"Department of ICT Integrated Ocean Smart Cities Engineering, Dong-A University, Busan 49315, Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"N.","family":"Yuvaraj","sequence":"additional","affiliation":[{"name":"Department of Architectural Engineering, Dong-A University, Busan 49315, Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"K. R.","family":"Sri Preethaa","sequence":"additional","affiliation":[{"name":"Department of Artificial Intelligence and Data Science, KPR Institute of Engineering and Technology, Coimbatore 641407, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Gang","family":"Hu","sequence":"additional","affiliation":[{"name":"School of Civil and Environmental Engineering, Harbin Institute of Technology, Shenzhen 518055, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9205-3836","authenticated-orcid":false,"given":"Dong-Eun","family":"Lee","sequence":"additional","affiliation":[{"name":"School of Architecture, Civil, Environment and Energy Engineering, Kyungpook National University, 80, Daehak-ro, Buk-gu, Daegu 41566, Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2021,4,3]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.jweia.2017.07.021","article-title":"Prediction of wind loads on high-rise building using a BP neural network combined with POD","volume":"170","author":"Dongmei","year":"2017","journal-title":"J. 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