# A Method for Detecting Pathologies in Concrete Structures Using Deep Neural Networks
## 1 Introduction
However, many incidents continue due to inadequate inspection and condition assessment. Visual inspection of surfaces is one of the primary methods for determining a reinforced concrete structure's physical and functional condition. This inspection should be performed regularly to ensure the structure continues functioning as intended. However, many incidents continue due to inadequate inspection and condition assessment This visual inspection is very time-consuming and poses a safety risk to inspectors. It is a method that is subjective to the skills, expertise, and experience of the engineers performing the inspection. Automated inspection methods have been developed using image acquisition and machine learning algorithms to overcome these limitations. This automation can result in a more cost-effective and efficient method and reduces the risk of workplace accidents. In addition, it can be objective and more reliable since the condition is defined by an algorithm being reproducible. Many studies have investigated the possibility of automated inspection of concrete structures using deep learning methods. The literature presents important works on concrete pathologies and artificial intelligence. Cascaded deep neural networks automatically detect image damage and cracks [4]. Research has explored how to improve crack detection and recognition based on Convolutional Neural Networks [5]. An overview of the challenges associated with automatic concrete crack detection in the presence of shadows is presented in [6]. The paper explains how to increase the robustness of material-specific deep learning models for crack detection in different materials [7]. Automated crack detection and analysis based on artificial neural networks has been used to inspect concrete structures [8]. Works have presented crack detection in concrete images using machine learning classification techniques [9]. Deep and convolutional neural networks have been compared in terms of image-based concrete surface crack detection [10]. Autonomous structural visual inspection has been presented using region-based deep learning to detect various types of damage [11]. Deep learning-based crack damage detection using convolutional neural networks is presented in [12]. Additionally, concrete crack detection based on deep learning image classification mentioning inspection when combined with unmanned aerial vehicles (UAV) is applied in [13]. These papers support pathology studies using artificial intelligence. Other papers in this line of research present relevant studies on using machine learning and concrete. The interest in monitoring the health of the existing bridge heritage has been combined with automation techniques using machine learning [14]. Vulnerability analysis of existing buildings indicating seismic vulnerability by exploiting available photographs with machine learning techniques is the topic of [15]. Quickly assessing the spatial distribution and severity of building damage for post-event emergency response and recovery using machine learning is achieved in [16]. This paper proposes two methods to detect and classify pathologies in images of concrete structures using convolutional neural networks. We investigated two possible approaches to deal with the reinforced concrete pathologies' automatic visual inspection. The first approach uses cropped images with a detected pathology to identify its type (classification). The other possibility is detecting in a reinforced concrete surface image of the specific regions where there are none or several pathologies (detection). The paper presents other work using classification with results close to those experienced. This paper proposes integrating classification dedicated to focused pathology images with detection in wide images of the structures.