In this study, we introduce a novel denoising transformer-based neural network (DTNN) model for predicting the remaining useful life (RUL) of lithium-ion batteries. The proposed DTNN model significantly outperforms traditional machine learning models and other deep learning architectures in terms of accuracy and reliability. Specifically, the DTNN achieved an $R^2$ value of 0.991, a mean absolute percentage error (MAPE) of 0.632%, and an absolute RUL error of 3.2, which are superior to other models such as Random Forest (RF), Decision Trees (DT), Multilayer Perceptron (MLP), Recurrent Neural Network (RNN), Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), Dual-LSTM, and DeTransformer. These results highlight the efficacy of the DTNN model in providing precise and reliable predictions for battery RUL, making it a promising tool for battery management systems in various applications.

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.

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.