## 5 Discussion and Conclusions  
This study explored the application of neural networks for ultrasound tomographic reconstruction of cementitious materials, particularly in the context of gradient damage assessments. The proposed approach leverages simulated training data to overcome the challenges associated with acquiring large real-world datasets, both in terms of cost and time. The ability to generate simulated datasets at a reduced economic and computational cost enables the creation of large, well-controlled datasets, facilitating complex training processes. Two neural network models were developed: a regression-based model for estimating material properties and a classification model for identifying different cement types. The results obtained from the simulations demonstrated high reconstruction accuracy, with Structural Similarity Index (SSIM) values exceeding 0.95, indicating strong performance for both models. These models effectively differentiated between materials, with well-defined boundaries when the contrast in acoustic properties was sufficient. With real cases, model 2 (classifier model) achieved superior performance in comparison to all the conventional tomographic reconstruction models in detecting the inner material. In the regression problem, the SSIM did not reach values (0.82) as high as in the classification problem (0.95), where the reconstruction of the material boundaries was significantly affected by a pronounced blurring effect. This undesired effect is an intrinsic effect of the problem itself because the activation function must be a linear activation function with a derivative lower than the sigmoidal activation function. When applied to real measurement data, the neural network maintained its effectiveness in cases with a single internal element. Although single-object cases are the least demanding scenario, traditional methods present problems when reconstructing them. This fact demonstrates the advantage of our approach over traditional tomographic reconstruction algorithms. In fact, compared with the traditional algorithms, the classification model achieved a better mean SSIM for the four specimens (0.713 \(\pm\) 0.067). Furthermore, the presence of multiple internal structures led to the reconstruction of artifacts and occasional misidentifications, primarily due to discrepancies between the real and simulated sinograms (see Figure 22), the low signal-to-noise ratios, and measurement system offsets. These discrepancies between the simulated and real data were attributed to the limitations of simulations to replicate all the processes that occur in reality. Nevertheless, the models' performance in the experiments that required a high investment of time and financial resources was remarkable. In addition, they allowed for experimentation with variables in a controlled environment. Finally, the use of these models can facilitate the creation of deep learning prototypes before training with real data. Despite these limitations, the model successfully captured the general morphology of internal structures, proving the feasibility of training a simple neural network using simulated data. The simplicity of the neural network together with the low variability represented in the simulation cases is likely to limit the performance of the model in demanding chaos. However, due to its simplicity, it can be easily implemented with embedded systems or low specification hardware and can be used for detecting a specific damage gradient. It is understood that the existence of a greater number of simulated cases of a higher quality (i.e., closer to reality) would facilitate the use of more complex neural models. In the domain of image reconstruction, convolutional autoencoders have demonstrated notable efficacy. However, it should be noted that these architectures are more data-hungry and require more computational power (usually GPU-accelerated). In practice, as already mentioned, more challenging cases may appear.