###### Abstract
End-to-end classification models with direct mapping to the output classes from raw input can be constructed without manual feature extraction to establish an intermediate feature space. In order to solve the problems of uneven inputs and difficulty in determining the failure threshold described earlier, this paper proposes the development of a deep neural network (DNN) for HIs formation from raw input. In this approach, a stacked autoencoder (SAE) is first utilized for pretraining to prevent the DNN from being trapped in local optimum by random initialization. The DNN's layers are pretrained successively in a layer-wise, unsupervised learning strategy. After a regression layer is added, the whole network is fine-tuned in a supervised manner with the labeled data. AE hits are detected in each signal segment with the constant false-alarm rate (CFAR) algorithm [4]; the number of AE hits can represent the damage growth in a concrete structure [4], and these values are also used as input data labels. In this study, a recurrent neural network (RNN) is used for RUL prediction. In comparison to a feedforward neural network, the data 'context' and data clustering for long-term prediction in an RNN are presented by competitive learning rules in input preprocessing [22]. To cope with conventional RNNs' poor long-term dependencies learning, long short-term memory (LSTM) is implemented in this research. LSTM is able to remember information for long periods of time via the introduced gates [22; 23]. Redundant information is disposed of by the forget gate; the key information is stored in the internal state after being chosen by the input gate; and the output information is then determined by the output gate. This architecture allows long-term storage, updating the key information efficaciously, and avoiding gradient vanishing. LSTM RNN has been widely and successfully applied for machine health monitoring [24], gas concentration prediction [25], marine temperature prediction [26], and wind power short-term prediction [27]. In this study, concrete degradation data with hundredsof segmented signals over the deterioration progression in concrete beams is considered long-term time-series data. With the availability of offline training data, an LSTM-RNN can be constructed to perform long-term dependencies learning on the concrete degradation progress. Then, with just an amount of online data, precise RLU prognosis on the specimen can be achieved with the trained LSTM-RNN. This paper presents the following contributions:  
1. A health indicator (HI) constructor based on a SAE-DNN is developed. No manually extracted features are used in the construction of the SAE-DNN-based HI constructor. The SAE-DNN automatically extracts representative features during the training process. 2. The constant false-alarm rate (CFAR) algorithm is used to capture the AE hits in every degradation cycle; the number of AE hits in each cycle is considered the label to train the DNN. 3. The LSTM-RNN is investigated to learn the long-term dependencies of HI curves constructed in an offline process and is then used to predict the RUL of a concrete structure in an online process. This paper is arranged as follows: Section 2 introduces the experimental setup with data descriptions and illustrations. Section 3 describes the SAE-DNN-based HI constructor thoroughly, along with the LSTM-RNN-oriented RUL prognosis. Section 4 gives the experimental results and analysis. Finally, Section 5 presents our conclusions.