# The Current Development of Structural Health Monitoring for Bridges: A Review
## 5 Damage Identification Methods
### Optimization Algorithm-Based Methods
#### 5.3.2 Artificial Neural Network  
As a powerful intelligent computing tool, an ANN has unique advantages in solving large complexity problems. ANNs can be effectively applied to SDI after training. [PERSON] et al. [160] studied the damage caused by load changes and proposed a neural network training method based on proper orthogonal decomposition (POM). A supervised learning method was used to distinguish POM changes caused by injury. In the experiment, the authors successfully identified the damage caused by the train load on the railway truss bridge. [PERSON] et al. [161] used vehicle response to train ANN. They obtained the predicted error value based on the actual response compared with the predicted vehicle response. Then, the error distribution variation obtained by GP was used to identify the possible damage. [PERSON] et al. [162] used transmissibility functions as the input data of ANN in the research on damage identification in Ca-Non Bridge in Vietnam. They used simulated vehicle response data from the bridge to verify the effectiveness of the method. [PERSON] et al. [163] developed a simple one-dimensional CNN and established three independent acceleration databases for training. It is worth noting that the proposed CNN can still accurately identify structural state changes without any processing of the training data. As one of the representative algorithms of deep learning, CNN's general architecture is given in Figure 8.  
In vibration-based SDI, the change of structural modal characteristics is an important damage index. [PERSON] et al. [164] proposed a two-stage method for damage identification in steel beam bridges. Firstly, the damage was located based on modal strain energy (MSE) and the damage index was obtained simultaneously, which was then used as an input parameter to train the ANN. [PERSON] et al. [165] improved the damage index of the input ANN. They first trained two ANNs using damage indexes based on modal flexibility and MSE, respectively, and then fused the training results of the two networks. The damage identification capability of the proposed method was verified in an experiment of a long-span arch bridge. [PERSON] et al. [166] proposed a new damage detection method for steel-concrete composite bridges. For steel beam units, they used damage indices based on MSE in ANNs, and for bridge decks, they used a loss index based on modal flexibility in ANNs. [PERSON] et al. [167] studied the anti-noise performance of three SDI methods based on MSE, modal flexibility, and modal curvature. They then trained an ANN on the modal \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Authors** & **Method** & **Feature and/or Advantage** & **SDI Level** \\ \hline [PERSON] and [PERSON] [156] & Chaotic moth–flame-invasive & Strong global search ability and high & \\  & weed optimization & convergence efficiency & 1–3 \\ \hline [PERSON] et al. [157] & Modified DBA & Elimination strategy to increase the diversity of & 1–3 \\  & & the population & \\ \hline [PERSON] et al. [158] & Enhanced WOA & Objective function based on flexible matrix & 1–3 * \\ \hline [PERSON] et al. [159] & FD–WOA & Good anti-noise ability & 1–3 \\ \hline \hline \end{tabular}  
*Quantization effect is affected by noise.  
\end{table} 
Table 5: _Cont._  
Figure 8: Architecture of the CNNs (ReLU is the activation function; FC is the fully connected layer). flexibility-based approach that performed best in anti-noise tests. A network with good noise resistance and damage quantification ability was obtained. [PERSON] et al. [168] used principal component analysis (PCA) to compress the frequency response data and feed it back to the ANN for damage prediction. [PERSON] et al. [169] proposed to apply the non-probabilistic method to PCA to reduce errors caused by various uncertainties and improve the efficiency of the frequency response data. Aiming to better avoid the limitations of using solely ANNs in SDI, some scholars combine the meta-heuristic algorithm with an ANN in their research. [PERSON] et al.