# A Method for Detecting Pathologies in Concrete Structures Using Deep Neural Networks
## 3 Experiments and Results
### Pathology Identification Using Cropped Images  
The first experiment applies a structured convolutional neural network to recognize the two classes present in the dataset. Figure 9 presents examples of images from the dataset containing cracks and images without cracks. The input receives 227 \(\times\) 227 pixels, three-channel RGB images. The network is fed with 20,000 images for each class, distributed as follows: 15,000 for training and 5000 for validation. Therefore, a total of 40,000 images is distributed evenly with and without cracks. This base is, therefore, balanced, containing the same number of images for the classes. The first hidden layer comprises 32 \(3\times 3\) filters. This layer makes use of the Relu activation function. Pooling follows this layer to reduce the dimensionality of the image. The next hidden layer comprises 64 \(3\times 3\) filters. This layer makes use of the Relu activation function. Pooling follows this layer to reduce the dimensionality of the image. Following the same definition, except for the number of filters, the next hidden layer comprises 128 3-by-3 filters. Again, this layer is followed by pooling to reduce the dimensionality of the image. This layer also makes use of the Relu activation feature. After the previous sequence, the Flatten feature places the data in a single dimension. This layer is followed by a dense layer of a thousand neurons. Finally, the Softmax activation function and a layer of two neurons are used. The use of two neurons occurs because the final output is a classification for the two intended classes--images of concrete with and without cracks. The training was performed with 50 epochs, aided by a GPU (Graphics Process Unit). The results obtained were satisfactory, with an accuracy of 99.43% using cross-validation. Figure 10 shows the result of one section. Additionally, in this figure, it can be seen that the training and validation converged after about 20 epochs. The training was performed with cross-validation. This technique prevents the choice of part of the base for training/validation from using a set that can vary greatly depending on the option. The dataset was divided into four training parts, leading to results of 99.45%, 99.43%, 99.41%, and 99.44%. This resulted in an average accuracy of 99.43% and a standard deviation of 0.017078. Figure 9: Images of the two types of classes in the dataset [18]. Figure 11 below shows the evolution of losses during training in one of the cross-validation steps. It is also possible to see that the progress is satisfactory, being concluded with low loss. Figure 11: Loss obtained from CNN. Figure 10: Accuracy obtained from CNN in one of the cross-validation steps. The literature presents similar work that can be used for comparison. Table 1 shows the results of other researchers so that there is a standard of comparison to indicate how good the result obtained is compared to different results. The tests performed allow the performance of the network to be evaluated. Initially, a set of one thousand images was used to measure performance. Table 2 shows the confusion matrix, from which the 99.4799% performance of the neural network is inferred. The ROC parameter AUC is 0.999876. AUC corresponds to the area under the ROC curve, Figure 12, where zero is the lousy value and one is the optimal value. According to Figure 13, an additional test with two pictures--one of each class, one with and one without crack--allows to analyze the ability of the network to classify an image individually. This test aims to simulate the situation where an individual image needs to be classified and in the case of multiple images, the same feature can be used in a loop. \begin{table}
\begin{tabular}{|c c|} \hline
**Author** & **Accuracy (\%)** \\ \hline [PERSON] [8] & 99.98 \\ [PERSON] [25] & 99.00 \\ Proposed Method & 99.43 \\ [PERSON] [9] & 98.60 \\ \hline [PERSON] [7] & 98.60 \\ [PERSON] [6] & 98.00 \\ \hline [PERSON] [4] & 95.92 \\ \hline \end{tabular}
\end{table}