Since abundant images can be generated in a building inspection, analyzing all these images by a professional can incur human error due to fatigue caused by such a tedious, repetitive task. Computer vision can, therefore, help with these monotonous and repetitive activities, resulting in time savings and reduced errors. This image analysis process for diagnosing pathologies in concrete structures can be modernized by automation using deep-learning methods. Deep learning has opened up significant possibilities for image processing in the construction industry. Although the available databases are still scarce in some areas, essential work on identifying pathologies in concrete is already available in the literature. The literature presents relevant work on pathologies in concrete structures and artificial intelligence. [PERSON] et al. [5] researched concrete exposed to fire. Cracks and fissures in concrete can especially occur in tunnels, where the temperature increase is significant. The result is that the safety of a tunnel is significantly compromised. The analysis of the parameters involved is complex since their analysis results in non-linear models that cannot easily be solved analytically. This article proposes the use of neural networks to solve this complex problem. The results are compared with experimental solutions to evaluate the efficiency of the proposed model. The study provides an important resource for studying this use case of concrete structures. [PERSON] et al. [6] emphasize the fact that one of the main activities of infrastructure maintenance is to assess damage to reinforced concrete elements. Corrosion is one of the pathologies that deserves the most attention, and maintenance activity requires major government investment in this area. Visual inspection is one possibility, but it is subjective and requires considerable time and resources. This research uses YOLO, version 3, to detect corrosion in reinforced concrete structures. Transfer learning and high-resolution images are adopted to improve the model's accuracy. The results show the satisfactory capacity of this artificial intelligence approach for civil engineering. [PERSON] et al. emphasize the importance of identifying and classifying cracks in concrete to avoid structural damage. They used neural networks to determine the behavior and types of failure in concrete cylinders. The methodology cataloged 2650 images of flaw types in concrete cylinders tested in compression in a Materials Testing and Strength Laboratory. The MobileNet, DenseNet121, ResNet50, and VGG16 algorithms were used with 96, 91, 86, and 90% accuracy, with the MobileNet algorithm being the best predictor with 96%. The work presents an algorithm that can help assess the health of concrete and can also be coupled with the use of drones [7]. [PERSON] et al. [8] highlight the importance of using computer vision for monitoring structures and that intelligent technologies are increasingly present in all phases of civil construction. A U-Net convolutional neural network (CNN) is used for the segmentation of hardened cement paste violations. The accuracy was 60%, but another model showed slightly better results. The work shows how this technology allows the efficient monitoring of hardened cement paste in relation to possible pathologies. [PERSON] et al. [9] use neural networks to predict the compressive strength of microbial concrete. Sixty sets of data were obtained by observing the strength of curing concrete over 7, 28, and 56 days. The network was trained with three input parameters to produce the compressive strength output. The experiment shows how accumulating bacteria and calcium lactate as a source of nutrients can increase compressive strength and heal cracks. The neural network experiment allows for predicting the resistance of microbial concrete with different concentrations of the bacteria Bacillus subtilis and calcium lactate. [PERSON] et al.