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Predicting displacement of bridge based on CEEMDAN-KELM model using GNSS monitoring data / Qian Fan in Journal of applied geodesy, vol 14 n° 3 (July 2020)  

Public [article]  inJournal of applied geodesy > vol 14 n° 3 (July 2020) . - pp 253 – 261 Titre : Predicting displacement of bridge based on CEEMDAN-KELM model using GNSS monitoring data Type de document : Article/Communication Auteurs : Qian Fan, Auteur ; Xiaolin Meng, Auteur ; Dinh Tung Nguyen, Auteur ; Yilin Xie, Auteur ; Jiayong Yu, Auteur Année de publication : 2020 Article en page(s) : pp 253 – 261 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Topographie moderne [Termes IGN] apprentissage automatique [Termes IGN] combinaison linéaire [Termes IGN] données GNSS [Termes IGN] méthode fondée sur le noyau [Termes IGN] pont [Termes IGN] prévention des risques [Termes IGN] série temporelle [Termes IGN] surveillance d'ouvrage Résumé : (auteur) Bridges are critical to economic and social development of a country. In order to ensure the safe operation of bridges, it is of great significance to accurately predict displacement of monitoring points from bridge Structural Health System (SHM). In the paper, a CEEMDAN-KELM model is proposed to improve the accuracy of displacement prediction of bridge. Firstly, the original displacement monitoring time series of bridge is accurately and effectively decomposed into multiple components called intrinsic mode functions (IMFs) and one residual component using a method named complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN). Then, these components are forecasted by establishing appropriate kernel extreme learning machine (KELM) prediction models, respectively. At last, the prediction results of all components including residual component are summed as the final prediction results. A case study using global navigation satellite system (GNSS) monitoring data is used to illustrate the feasibility and validity of the proposed model. Practical results show that prediction accuracy using CEEMDAN-KELM model is superior to BP neural network model, EMD-ELM model and EMD-KELM model in terms of the same monitoring data. Numéro de notice : A2020-396 Affiliation des auteurs : non IGN Thématique : POSITIONNEMENT Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1515/jag-2019-0057 Date de publication en ligne : 27/03/2020 En ligne : https://doi.org/10.1515/jag-2019-0057 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95431 [article]