Affiliation(s):
School of Architectural Engineering, Hunan Institute of Engineering, Xiangtan 411104, China;
moreAffiliation(s): School of Architectural Engineering, Hunan Institute of Engineering, Xiangtan 411104, China; School of Civil Engineering, Changsha University of Science & Technology, Changsha 410114, China; School of Civil Engineering, Central South University of Forestry and Technology, Changsha 410004, China;
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Benkun TAN, Da WANG, Jialin SHI, Lianqi ZHANG. Temperature field prediction of steel-concrete composite decks using TVFEMD-Stacking ensemble algorithm[J]. Journal of Zhejiang University Science A,in press.Frontiers of Information Technology & Electronic Engineering,in press.https://doi.org/10.1631/jzus.A2300441
@article{title="Temperature field prediction of steel-concrete composite decks using TVFEMD-Stacking ensemble algorithm", author="Benkun TAN, Da WANG, Jialin SHI, Lianqi ZHANG", journal="Journal of Zhejiang University Science A", year="in press", publisher="Zhejiang University Press & Springer", doi="https://doi.org/10.1631/jzus.A2300441" }
%0 Journal Article %T Temperature field prediction of steel-concrete composite decks using TVFEMD-Stacking ensemble algorithm %A Benkun TAN %A Da WANG %A Jialin SHI %A Lianqi ZHANG %J Journal of Zhejiang University SCIENCE A %P %@ 1673-565X %D in press %I Zhejiang University Press & Springer doi="https://doi.org/10.1631/jzus.A2300441"
TY - JOUR T1 - Temperature field prediction of steel-concrete composite decks using TVFEMD-Stacking ensemble algorithm A1 - Benkun TAN A1 - Da WANG A1 - Jialin SHI A1 - Lianqi ZHANG J0 - Journal of Zhejiang University Science A SP - EP - %@ 1673-565X Y1 - in press PB - Zhejiang University Press & Springer ER - doi="https://doi.org/10.1631/jzus.A2300441"
Abstract: This research aims to develop an advanced deep learning-based ensemble algorithm, utilizing environmental temperature and solar radiation as feature factors, to conduct hourly temperature field predictions for steel-concrete composite decks (SCCDs). The proposed model comprises feature parameter lag selection, two non-stationary time series decomposition methods (Empirical mode decomposition (EMD) and time-varying filtering empirical mode de-composition (TVFEMD)), and a stacking ensemble prediction model. To validate the proposed model, five machine learning (ML) models (random forest (RF), support vector regression (SVR), multilayer perceptron (MLP), gradient boosting regression (GBR) and extreme gradient boosting (XGBoost)) were tested as base learners and evaluations were conducted within independent, mixed, and ensemble frameworks. Finally, predictions are made based on engineering cases. The results indicate that consideration of lag variables and modal decomposition can significantly improve the prediction performance of learners, and the stacking framework, which combines multiple learners, achieves superior prediction results. The proposed method demonstrates a high degree of predictive robustness and can be applied to statistical analysis of the temperature field in SCCDs. Incorporating time lag features helps account for the delayed heat dissipation phenomenon in concrete, while decomposition techniques assist in feature extraction.
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