###### Abstract  
The shallow features extracted by the traditional artificial intelligence algorithm-based damage identification methods pose low sensitivity and ignore the timing characteristics of vibration signals. Thus, this study uses the high-dimensional feature extraction advantages of convolutional neural networks (CNNs) and the time series modeling capability of long short-term memory networks (LSTM) to identify damage to long-span bridges. Firstly, the features extracted by CNN and LSTM are fused as the input of the fully connected layer to train the CNN-LSTM model. After that, the trained CNN-LSTM model is employed for damage identification. Finally, a numerical example of a large-span suspension bridge was carried out to investigate the effectiveness of the proposed method. Furthermore, the performance of CNN-LSTM and CNN under different noise levels was compared to test the feasibility of application in practical engineering. The results demonstrate the following: (1) the combination of CNN and LSTM is satisfactory with 94% of the damage localization accuracy and only 8.0% of the average relative identification error (ARIE) of damage severity identification; (2) in comparison to the CNN, the CNN-LSTM results in superior identification accuracy; the damage localization accuracy is improved by 8.13%, while the decrement of ARIE of damage severity identification is 5.20%; and (3) the proposed method is capable of resisting the influence of environmental noise and acquires an acceptable recognition effect for multi-location damage; in a database with a lower signal-to-noise ratio of 3.33, the damage localization accuracy of the CNN-LSTM model is 67.06%, and the ARIE of the damage severity identification is 31%. This work provides an innovative idea for damage identification of long-span bridges and is conducive to promote follow-up studies regarding structural condition evaluation. long-span bridge; long short-term memory network; convolutional neural network; damage identification +
Footnote †: journal: Applied Soft Computing
(i) extraction of damage sensitive features (DSFs). Time series analysis, frequency spectrum analysis, statistical analysis, and other means are used to extract features that characterize the damage state from the structural response [4; 5]; (ii) prediction of damage. Artificial intelligence algorithms are employed to establish the mapping relationship between DSFs and damage location and severity [6; 7]. For instance, [PERSON] et al. [8] utilized probabilistic logic methods to develop a two-step Bayesian framework for damage localization and extent prediction of the suspension bridge main cables and hangers. [PERSON] et al. [9] applied the firefly and the artificial bee colony algorithms to diagnose the damage caused by the decrease in stiffness of the cable and main beam. Due to the high uncertainty of the measurement and finite element modeling, [PERSON] et al. [10] established the objective function using modal data and sparse regularization technology, and they developed a hybrid group intelligence technology involving the Jaya and tree-seed algorithm to identify structural damage. [PERSON] et al. [11] used modal flexibility as DSF and performed damage diagnosis of suspension bridge hangers based on a genetic algorithm. [PERSON] et al. [12] recruited wavelet coefficient modulus maxima as DSF and employed an artificial neural network (ANN) and particle swarm algorithm to complete the damage identification of a suspension bridge. [PERSON] et al. [13] developed a two-step structural damage identification method. First, the support vector machine was used to initially determine the damage location to reduce the search space dimension, and then, the evolutionary algorithm was used to accurately and quickly search for the damage location. The above study shows that current research on conventional artificial intelligence algorithms-based damage identification methods mainly focuses on traditional machine learning and evolutionary algorithms.