## 1 Introduction  
Concrete is one of the most widely used materials in the construction industry, and its fatigue damage seriously affects the safety of structures such as crane girders [1], pavements [2], and bridges [3]. Accurate prediction of fatigue life is of great engineering significance for the safety of concrete structures [4, 5]. The fatigue life of concrete is affected by many complex factors, such as material, load, and environment. These factors make it difficult to establish an accurate concrete fatigue life prediction model. The classical methods for predicting the fatigue life of concrete mainly include fracture mechanics, continuum damage mechanics, and energy methods [6, 7]. However, due to the influence of empirical parameters and specific materials, these mathematical and physical modeling methods have low prediction accuracy and poor generalizability. At present, the application of artificial intelligence has developed in various research fields of machinery [8, 9, 10], medicine [11, 12, 13, 14], and civil engineering [15]. Many scholars use support vector machine (SVM) [16], decision trees [17], random forests [18], and adaptive boosting (AdaBoost) [19] to predict the mechanical strength properties and mix proportions of concrete and obtain higher prediction accuracy than traditional models. However, there is still room for improvement in the prediction accuracy of these statistical probability-based models in complex and highly uncertain data. In recent years, artificial neural network (ANN) has been widely used due to its powerful nonlinear fitting ability. ANN does not require functional assumptions and can learn complex nonlinear relationships through a data-driven training process, which is advantageous in fatigue life prediction research [20]. There are many studies on concrete fatigue life prediction using ANN, and higher prediction accuracy has been achieved in experiments [21,22]. Therefore, ANN is considered a suitable method to solve the problem of concrete fatigue life prediction with high uncertainty. The high prediction accuracy of the ANN model largely depends on sufficient training data. However, the fatigue life of concrete is usually determined by experiments, and the available training data are limited. When the training set is too small, the model will overfit the distribution trend of the training set and cannot grasp the development trend of concrete fatigue life, resulting in an overfitting phenomenon [23]. Data augmentation is an effective tool to increase the number of training sets, which increases the amount of data used for model training by generating synthetic data to improve the prediction accuracy of the model [24]. Classic sequential data augmentation methods include temporal transformations, statistical generative models, and learning-based models [25]. Time-domain transformation methods mainly include sampling, slicing, flipping, etc., but it is not easy to confirm whether this method affects the sequence distribution [26]. Statistical generative models such as mixed autoregressive (MAR) [27], use statistical models to simulate the distribution of data, but they rely too much on the initial value. Once the initial value is disturbed, the data will be generated according to different conditional distributions. Learning-based models, such as generative adversarial networks (GANs) [28,29], evolutionary search [30], etc., generate augmented data based on the exact fit of the generator to the distribution of the source data and are currently widely used in the image domain. [PERSON] et al. proposed a deep convolutional conditional generation adversarial network to generate MIP positron emission tomography (PET) images, which solved the problem of category imbalance and lack of data in medical imaging [29]. However, in augmentation of series data, such learning-based models perform unstably in too small datasets [31,32]. Given this, [PERSON] et al.