首页|Temperature field prediction of steel-concrete composite decks using TVFEMD-stacking ensemble algorithm

Temperature field prediction of steel-concrete composite decks using TVFEMD-stacking ensemble algorithm

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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-based empirical mode decomposition(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.

Steel-concrete composite deck(SCCD)Temperature fieldTime-varying filtering-based empirical mode decomposition(TVFEMD)Feature selectionMachine learning(ML)

Benkun TAN、Da WANG、Jialin SHI、Lianqi ZHANG

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School of Civil Engineering,Changsha University of Science&Technology,Changsha 410114,China

School of Civil and Architecture Engineering,Hunan University of Arts and Science,Changde 415000,China

School of Civil Engineering,Central South University of Forestry and Technology,Changsha 410004,China

School of Civil Engineering,Central South University of Forestry and Technology,Changsha,410004,China

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2024

10.1631/jzus.A2300441

浙江大学学报(英文版)(A辑:应用物理和工程)
浙江大学

浙江大学学报(英文版)(A辑:应用物理和工程)

CSTPCD
影响因子：0.556
ISSN：1673-565X
年,卷(期)：2024.25(9)