# Machine-Learning-Based Methods for Acoustic Emission Testing: A Review
## 5 Comparison between ML-Based Methods for AE  
In the previous sections, we have collected various contributions from the scientific community that have adopted methodologies based on ML to address the problem of structural health monitoring using AE. To make a comparison between the different technologies available, it is appropriate to apply the different algorithms on the same dataset. In this regard, a dataset was used that contains the measurements using AE sensors performed on concrete specimens [105]. The characteristics of the instrumentation used for data collection are shown in Table 5. The tests were carried out on concrete specimens with dimensions \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Reference** & **Input** & **RNN Type** & **Accuracy** \\ \hline [PERSON] et al. [95] & AE from microseismic wave & LSTM & 70–80\% \\ [PERSON] et al. [96] & AE from sliding bearings & LSTM & - \\ [PERSON] et al. [97] & AE from uniaxial loading of a & Two-step RNN & 97\% \\ [PERSON] et al. [102] & AE from rail crack & NARX & - \\ [PERSON] et al. [99] & AE from multiple & LSTM & 100\% \\ [PERSON] et al. [100] & AE from loaded & LSTM & - \\ [PERSON] et al. [98] & Vibration signals & GRU & 98\% \\ [PERSON] et al. [101] & AE from diamond & LSTM & - \\ [PERSON] et al. [104] & AE from pencil-lead breaks & LSTM & 80\% \\ [PERSON] et al. [103] & AE from machine functioning & LSTM + ANN & - \\ \hline \hline \end{tabular}
\end{table} 
Table 4: RNN-based methods for AE testing. of 15 \(\times\) 15 \(\times\) 15 cm left for 28 days at a temperature of about 20 \({}^{\circ}\)C and humidity of 95%. The specimen was subjected to a nondestructive compression test to stress the formation of cracks. The data were subsequently labeled by grouping them into three classes: tensile, shear, and mixed. Several ML-based algorithms have been applied to address the data classification problem. The dataset consists of 1650 records and 1000 features. For the validation of the results, k-fold cross-validation was applied, one of the most widespread validation techniques of ML models, used to quantify the accuracy of the prediction and is a good preventive measure against overfitting. If the sample dataset is limited due to experimental problems or the impossibility of repeating the experiment to obtain a greater number of examples with which to train the algorithm, a method of splitting the algorithms is often used. Data are available in different categories. In the method called K-fold, the dataset available at the beginning of the experiment is divided into K groups, of which K-1 are used for the training and the remaining group for the generalization test. This procedure is repeated for all the K groups chosen, varying each time the group chosen for generalization. This has the advantage that all examples are used, at least once, for both training and testing. On a practical level, this method is very similar to that of the bootstrap type and has the advantage of being able to estimate the characteristic parameters of pattern recognition by distributing the results obtained. A problem inherent in the method lies in the calculation speed, as it could often be expensive to go to evaluate a large number of programs runs for all possible K's. In our case, a cross-validation with five folds was applied. Table 6 summarizes the characteristics of the classification algorithms adopted and the results in terms of accuracy. The following algorithms were applied.