###### Abstract  
Remaining useful life (RUL) prognosis is one of the most important techniques in concrete structure health management. This technique evaluates the concrete structure strength through determining the advent of failure, which is very helpful to reduce maintenance costs and extend structure life. Degradation information with the capability of reflecting structure health can be considered as a principal factor to achieve better prognosis performance. In traditional data-driven RUL prognosis, there are drawbacks in which features are manually extracted and threshold is defined to mark the specimen's breakdown. To overcome these limitations, this paper presents an innovative SAE-DNN structure capable of automatic health indicator (HI) construction from raw signals. HI curves constructed by SAE-DNN have much better fitness metrics than HI curves constructed from statistical parameters such as RMS, Kurtosis, Sknewness, etc. In the next stage, HI curves constructed from training degradation data are then used to train a long short-term memory recurrent neural network (LSTM-RNN). The LSTM-RNN is utilized as a RUL predictor since its special gates allow it to learn long-term dependencies even when the training data is limited. Model construction, verification, and comparison are performed on experimental reinforced concrete (RC) beam data. Experimental results indicates that LSTM-RNN generally estimates more accurate RULs of concrete beams than GRU-RNN and simple RNN with the average prediction error cycles was less than half compared to those of the simple RNN. s 2021 11 4113 11  
contrast, estimating RUL with available data in data-driven methods does not need the exact failure mechanisms for data-driven modeling. Therefore, they have been in favor in recent studies [12]. The data-driven approach to prognosis can be divided into three stages: data acquisition, construction of health indicators (HIs), and then prognostics. To monitor and highlight the fracture occurrences, acoustic emission (AE) is commonly used for data acquisition, being able to detect both external and internal damage on the structure [13; 14; 15]. Therefore, AE is utilized here to study the deterioration of a concrete structure during a loading process until its final failure. The second stage, which constructs HIs, can strongly affect the accuracy of the RUL [9]. Most data-driven methods form HIs from statistical parameters, among which kurtosis and root mean square (RMS) are used most often. [PERSON] et al. [16] proposed the direct use of these two parameters for bearing vibration signal quantification, while [PERSON] [17] suggested spectral kurtosis in bearing health monitoring, which is considered one of the most intriguing and effective HI construction methods. In this approach, the kurtosis can be calculated by the fourth-order central moment over the squared second-order central moment. In addition to these two parameters, the smoothness index [18] is also important. This parameter is the result of the geometric mean over the arithmetic mean of the modulus of the wavelet coefficients. In recent years, synthesized HIs have attracted increasing attention [19]. They are often formed by data fusion techniques that combine multi-dimensional features such as RMS, kurtosis, and variance into one-dimensional HIs [9; 10; 20]. Even though synthesized HIs have achieved spectacular performance and results, there are still disadvantages to be overcome. The first major problem is the uneven contribution of inputs to HI construction, which is the consequence of different features having different ranges. The second major problem is the difficulty in determining the failure threshold, which depends on the specific specimen at hand. Deep learning techniques have recently been successfully applied in many areas, including natural language processing, computer vision, and robotics [21; 22]. They have displayed a promising competence to extract features automatically during the requisite training period.