A parameterless approach for long-term creep prediction in concrete using hybrid CNN-transformer model

The creep behavior of concrete, a critical long-term property in construction, significantly impacts material selection and structural design in civil engineering. Conventional methods for predicting creep usually require many parameters, presenting two significant challenges: substantial consumption of sensor resources and the inability of limited static parameters to capture the material’s complexity. This study introduces a groundbreaking parameterless approach namely Creepformer to predict long-term creep using only early concrete creep data and proposes a simple but effective hybrid model combining convolutional neural networks (CNN) and Transformers within an encoder–decoder framework. The encoder part of the model is composed of the residual neural network, which is responsible for noise reduction, local correlation learning, and embedding feature compression. The decoder consists of a transformer decoder to capture the long-distance dependencies of the time series and global features. Our model achieved impressive performance, with a root mean squared error (RMSE) of 8.28, a mean absolute error (MAE) of 6.85 on the Northwestern University database, an RMSE of 10.09, and an MAE of 7.10 on the real-world Yihui dataset. In addition, we proposed a data completion tool based on random forest to ensure the consistency of data intervals and enrich data samples in different environments and situations compared with other studies. The data completion tool has been tested with RMSE 3.2 and MAE 2.4. Furthermore, the model was subjected to an ablation experiment to prove the effectiveness of the encoder. Finally, the model is applied as two case studies in real-world ordinary concrete and ultrahigh-performance concrete experiments.