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

Abstract

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%.

Graphical abstract

Keywords

concrete creep / uncertainty prediction / hybrid method / data-driven / model-driven / convolutional neural network

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Electronic Supplementary Material

Supplementary material is available in the online version of this article at https://doi.org/10.1007/s11709-024-1104-0 and is accessible for authorized users.

Acknowledgements

This study was supported by the National Natural Science Foundation of China (Grant Nos. 52208166 and 52108135), the National Key Research and Development Program of China (No. 2021YFB2600900), the Science and Technology Innovation Program of Hunan Province (No. 2022RC1186), and the Aid program for Science and Technology Innovative Research Team in Higher Educational Institutions of Hunan Province.

Competing interests

The authors declare that they have no competing interests.

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