# A Method for Detecting Pathologies in Concrete Structures Using Deep Neural Networks
## 1 Introduction  
Civil Construction is of enormous relevance for its relationship with the development of urban centers and for representing an important sector for the economy, generating investments and jobs [1]. The maintenance of buildings is fundamental because of their direct relationship with people's safety. Inadequate, or even non-existent, maintenance can result in the loss of a building or, even worse, in accidents to users, which can even result in severe and fatal accidents. Reinforced concrete is the world's second most used building material, providing a versatile, durable, affordable, functional, and attractive construction [2]. Its high compressive strength property allows this material to resist this very present stress in structures, and its deficiency in tensile strength can be compensated with its association with steel [1]. The possibility of easily molding concrete, even on the site where it will be used, allows a versatility that also provides a desirable advantage that places it as one of the main alternatives in building construction. It is a very durable building material but can deteriorate due to exposure to severe conditions associated with the environment, loading, effects ofaggressive actions, embedded metal corrosion, frost overloading, concrete resistance to volume changes, abrasion/erosion, and chemical actions. This maintenance is essential for reinforced concrete structures, given their wide use and the collapse impacts of these structures. Although concrete has enormous advantages, it is subject to pathologies that can compromise its strength. The great danger is that concrete structures lose the calculated resistance for which they were designed. For this reason, it must be monitored to prevent the onset or worsening of pathologies that may develop throughout life. There are several techniques to monitor concrete structures and identify their pathologies, such as instrumentation, image analysis, and on-site inspection, which is very usual for inspecting its surface because most of the concrete pathologies end up representing specific characteristics in its surface [3]. Several pathologies can be analyzed via images of concrete structures since their effects can appear on the surface [3]. This is, for example, the case of fissures or cracks, fragmentation of part of the concrete, efflorescence, corrosion stains, and exposed steel bars. These pathologies are explained along with image presentation in the images section. Cracks represent one of the main concerns with concrete. This paper works on the classification with cropped images of the pathology of cracks. The largest availability of datasets is related to this pathology exactly because of its importance. These justifications result in a greater need for understanding this pathology. The cracks can be observed according to the form of the manifest in \"geometric or isolated\" or \"mapped or disseminated\". According to their activity, they can be \"active or alive\" or \"passive\". The cracks can also be observed according to the variation of their opening in \"seasonal\" or \"progressive\". The way cracks appear varies significantly, but observing the manifestation is fundamental for a study that leads to understanding this pathology. The design formed by the cracks results in the first geometric or disseminated definition. This first observation can be subdivided into active or passive, depending on whether the activity is progressing or has stabilized. Finally, concerning the time that the crack varies its opening or closing, around an average value, they can be observed as seasonal or progressive. The causes of these variations need to be investigated; for example, the opening can oscillate due to temperature or humidity variation. Manual visual inspection is one of the primary methods for determining a wired concrete structure's physical and functional condition. This inspection should be performed regularly to ensure the structure continues functioning as intended.