首页|Hybrid model-driven and data-driven method for predicting concrete creep considering uncertainty quantification

Hybrid model-driven and data-driven method for predicting concrete creep considering uncertainty quantification

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Reasonable prediction of concrete creep is the basis of studying long-term deflection of concrete structures.In this paper,a hybrid model-driven and data-driven(HMD)method for predicting concrete creep is proposed by using the sequence integration strategy.Then,a novel uncertainty prediction model(UPM)is developed considering uncertainty quantification.Finally,the effectiveness of the proposed method is validated by using the North-western University(NU)database of creep,and the effect of uncertainty on prediction results are also discussed.The analysis results show that the proposed HMD method outperforms the model-driven and three data-driven methods,including the genetic algorithm-back propagation neural network(GA-BPNN),particle swarm optimization-support vector regression(PSO-SVR)and convolutional neural network only method,in accuracy and time efficiency.The proposed UPM of concrete creep not only ensures relatively good prediction accuracy,but also quantifies the model and measurement uncertainties during the prediction process.Additionally,although incorporating measurement uncertainty into concrete creep prediction can improve the prediction performance of UPM,the prediction interval of the creep compliance is more sensitive to model uncertainty than to measurement uncertainty,and the mean contribution of variance attributed to the model uncertainty to the total variance is about 90％.

concrete creepuncertainty predictionhybrid methoddata-drivenmodel-drivenconvolutional neural network

Yiming YANG、Chengkun ZHOU、Jianxin PENG、Chunsheng CAI、Huang TANG、Jianren ZHANG

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School of Civil Engineering,Hunan City University,Yiyang 413000,China

Hunan Engineering Research Center of Development and Application of Ceramsite Concrete Technology,Hunan City University,Yiyang 413000,China

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

Department of Bridge Engineering,School of Transportation,Southeast University,Nanjing 211189,China

Department of Civil and Environmental Engineering,Louisiana State University,Baton Rouge,LA 70803,USA

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2024

10.1007/s11709-024-1104-0

结构与土木工程前沿
高等教育出版社

结构与土木工程前沿

CSTPCDEI
影响因子：0.082
ISSN：2095-2430
年,卷(期)：2024.18(10)