Enhancing Concrete Creep Prediction With Deep Learning: A Soft-Sorted One-Dimensional CNN Approach

Creep is a long-term property of concrete that poses a significant challenge to structural engineering. The research content of this paper concerns how to accurately predict the creep behavior of concrete. The overall purpose is to develop a robust predictive model based on the Northwestern University (NU) database that overcomes the challenges encountered when handling tabular creep data, including the lack of spatial correlations between features and the presence of noise. To achieve this goal, a novel deep learning methodology that leverages convolutional neural networks (CNNs) is introduced. First, the data preprocessing phase involves feature selection and K-nearest neighbor-based data filling to enhance the quality of the given data. A soft-sorted 1D CNN is introduced to transform tabular data into multichannel images, thereby taking advantage of spatial locality and feature sorting. Additionally, this model incorporates noise reduction mechanisms through residual network encoders, achieving enhanced data clarity and model accuracy. The mean absolute error (MAE) and root mean square deviation (RMSE) of the model increase from 7.48 and 10.76 to 5.83 and 8.88, respectively. The major findings of this research indicate a significant predictive accuracy improvement over the existing methods. The model effectively captures the complex relationships between concrete creep and a range of influencing factors, enabling precise and quantitative predictions to be obtained. These findings offer deep insights for the field of civil engineering, enabling enhanced structural design, safety, and performance prediction in concrete applications. This paper contributes to the ongoing exploration of advanced deep learning techniques for addressing complex challenges in the domains of concrete mechanics and structural engineering.