{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,24]],"date-time":"2026-03-24T17:41:13Z","timestamp":1774374073981,"version":"3.50.1"},"reference-count":43,"publisher":"Wiley","issue":"1","license":[{"start":{"date-parts":[[2021,4,20]],"date-time":"2021-04-20T00:00:00Z","timestamp":1618876800000},"content-version":"vor","delay-in-days":109,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Key R&D Program of China","award":["2018YFC0808706"],"award-info":[{"award-number":["2018YFC0808706"]}]}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Computational Intelligence and Neuroscience"],"published-print":{"date-parts":[[2021,1]]},"abstract":"<jats:p>The structural engineering is subject to various subjective and objective factors, the deformation is usually inevitable, the deformation monitoring data usually are nonstationary and nonlinear, and the deformation prediction is a difficult problem in the field of structural monitoring. Aiming at the problems of the traditional structural deformation prediction methods, a structural deformation prediction model is proposed based on temporal convolutional networks (TCNs) in this study. The proposed model uses a one\u2010dimensional dilated causal convolution to reduce the model parameters, expand the receptive field, and prevent future information leakage. By obtaining the long\u2010term memory of time series, the internal time characteristics of structural deformation data can be effectively mined. The network hyperparameters of the TCN model are optimized by the orthogonal experiment, which determines the optimal combination of model parameters. The experimental results show that the predicted values of the proposed model are highly consistent with the actual monitored values. The average RMSE, MAPE, and MAE with the optimized model parameters reduce 44.15%, 82.03%, and 66.48%, respectively, and the average running time is reduced by 45.41% compared with the results without optimization parameters. The average RMSE, MAE, and MAPE reduce by 26.88%, 62.16%, and 40.83%, respectively, compared with WNN, DBN\u2010SVR, GRU, and LSTM models.<\/jats:p>","DOI":"10.1155\/2021\/8829639","type":"journal-article","created":{"date-parts":[[2021,4,21]],"date-time":"2021-04-21T03:34:00Z","timestamp":1618976040000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":34,"title":["A Deep Learning Prediction Model for Structural Deformation Based on Temporal Convolutional Networks"],"prefix":"10.1155","volume":"2021","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1116-1438","authenticated-orcid":false,"given":"Xianglong","family":"Luo","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9360-2777","authenticated-orcid":false,"given":"Wenjuan","family":"Gan","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0797-3596","authenticated-orcid":false,"given":"Lixin","family":"Wang","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0214-5169","authenticated-orcid":false,"given":"Yonghong","family":"Chen","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0222-9701","authenticated-orcid":false,"given":"Enlin","family":"Ma","sequence":"additional","affiliation":[]}],"member":"311","published-online":{"date-parts":[[2021,4,20]]},"reference":[{"key":"e_1_2_9_1_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.jbusres.2015.03.031"},{"key":"e_1_2_9_2_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.advengsoft.2013.06.019"},{"key":"e_1_2_9_3_2","doi-asserted-by":"publisher","DOI":"10.1016\/s1003-6326(13)62717-x"},{"key":"e_1_2_9_4_2","doi-asserted-by":"publisher","DOI":"10.4028\/www.scientific.net\/amm.80-81.516"},{"key":"e_1_2_9_5_2","doi-asserted-by":"publisher","DOI":"10.1007\/s10346-012-0350-8"},{"key":"e_1_2_9_6_2","doi-asserted-by":"publisher","DOI":"10.1007\/s12303-014-0012-z"},{"key":"e_1_2_9_7_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.enggeo.2011.03.003"},{"key":"e_1_2_9_8_2","doi-asserted-by":"publisher","DOI":"10.1002\/stc.2170"},{"key":"e_1_2_9_9_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.enggeo.2017.11.014"},{"key":"e_1_2_9_10_2","first-page":"1","article-title":"Introduction to grey system theory","volume":"1","author":"Deng J.","year":"1989","journal-title":"Journal of Grey System"},{"key":"e_1_2_9_11_2","first-page":"53","article-title":"Landslide deformation prediction based on grey and fuzzy-Markov chain model","volume":"35","author":"Zhu H. 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