Damage Identification of Long-Span Bridges Using the Hybrid of Convolutional Neural Network and Long Short-Term Memory Network

• Published in: Algorithms Vol. 14; no. 6; p. 180
• Main Authors: Fu, Lei, Tang, Qizhi, Gao, Peng, Xin, Jingzhou, Zhou, Jianting
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.06.2021
• Subjects: Acceptable noise levels; Algorithms; Artificial intelligence; Artificial neural networks; Background noise; Bridges; convolutional neural network; Damage detection; damage identification; Damage localization; Deep learning; Feature extraction; Genetic algorithms; Identification; Identification methods; long short-term memory network; long-span bridge; Machine learning; Methods; Model accuracy; Neural networks; Noise levels; Signal to noise ratio; Suspension bridges; Time series
• ISSN: 1999-4893; 1999-4893
• DOI: 10.3390/a14060180

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.