{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,25]],"date-time":"2026-03-25T16:20:11Z","timestamp":1774455611121,"version":"3.50.1"},"reference-count":48,"publisher":"MDPI AG","issue":"10","license":[{"start":{"date-parts":[[2022,5,16]],"date-time":"2022-05-16T00:00:00Z","timestamp":1652659200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Natural Science Foundation of China","award":["Grant Nos. 52178115 and 51708112"],"award-info":[{"award-number":["Grant Nos. 52178115 and 51708112"]}]},{"name":"National Natural Science Foundation of China","award":["SKL-IOTSC(UM)-2021-2023 and 0094\/2021\/A2"],"award-info":[{"award-number":["SKL-IOTSC(UM)-2021-2023 and 0094\/2021\/A2"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Seismic response prediction is a challenging problem and is significant in every stage during a structure\u2019s life cycle. Deep neural network has proven to be an efficient tool in the response prediction of structures. However, a conventional neural network with deterministic parameters is unable to predict the random dynamic response of structures. In this paper, a deep Bayesian convolutional neural network is proposed to predict seismic response. The Bayes-backpropagation algorithm is applied to train the proposed Bayesian deep learning model. A numerical example of a three-dimensional building structure is utilized to validate the performance of the proposed model. The result shows that both acceleration and displacement responses can be predicted with a high level of accuracy by using the proposed method. The main statistical indices of prediction results agree closely with the results from finite element analysis. Furthermore, the influence of random parameters and the robustness of the proposed model are discussed.<\/jats:p>","DOI":"10.3390\/s22103775","type":"journal-article","created":{"date-parts":[[2022,5,16]],"date-time":"2022-05-16T21:36:06Z","timestamp":1652736966000},"page":"3775","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":33,"title":["Probabilistic Seismic Response Prediction of Three-Dimensional Structures Based on Bayesian Convolutional Neural Network"],"prefix":"10.3390","volume":"22","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1808-1748","authenticated-orcid":false,"given":"Tianyu","family":"Wang","sequence":"first","affiliation":[{"name":"School of Civil Engineering, Southeast University, Nanjing 211189, China"},{"name":"International Institute of Urban Systems Engineering (IIUSE), Southeast University, Nanjing 211189, China"},{"name":"National and Local Joint Engineering Research Center for Intelligent Construction and Maintenance, Southeast University, Nanjing 211189, China"}]},{"given":"Huile","family":"Li","sequence":"additional","affiliation":[{"name":"School of Civil Engineering, Southeast University, Nanjing 211189, China"},{"name":"National and Local Joint Engineering Research Center for Intelligent Construction and Maintenance, Southeast University, Nanjing 211189, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2793-5194","authenticated-orcid":false,"given":"Mohammad","family":"Noori","sequence":"additional","affiliation":[{"name":"Department of Mechanical Engineering, California Polytechnic State University, San Luis Obispo, CA 93407, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6132-4920","authenticated-orcid":false,"given":"Ramin","family":"Ghiasi","sequence":"additional","affiliation":[{"name":"International Institute of Urban Systems Engineering (IIUSE), Southeast University, Nanjing 211189, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7363-6761","authenticated-orcid":false,"given":"Sin-Chi","family":"Kuok","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Internet of Things for Smart City, Guangdong-Hong Kong-Macau Joint Laboratory for Smart City, Department of Civil and Environmental Engineering, University of Macau, Macau, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3618-1187","authenticated-orcid":false,"given":"Wael A.","family":"Altabey","sequence":"additional","affiliation":[{"name":"International Institute of Urban Systems Engineering (IIUSE), Southeast University, Nanjing 211189, China"},{"name":"Department of Mechanical Engineering, Faculty of Engineering, Alexandria University, Alexandria 21544, Egypt"}]}],"member":"1968","published-online":{"date-parts":[[2022,5,16]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1151","DOI":"10.1016\/j.engstruct.2005.02.015","article-title":"Probability-based seismic response analysis","volume":"27","author":"Aslani","year":"2005","journal-title":"Eng. 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