## 1 Introduction  
Since time immemorial railway infrastructure networks were developed in industrial countries, they have been based the demands of transferring loads and passengers. As such, passengers and infrastructure owners are the most substantial stakeholders and it is necessary to undertake corrective actions in line with stakeholders' demands. For instance, based on operators' mandates and passengers' expectations, enhancing the convenience of travel and reducing time travel duration is essential. Therefore, automation in railway infrastructure quality control is an important factor in reaching the mentioned goals with a minimum risk for inspectors. Additionally, reducing the number of unexpected failures during operations leads to reducing the train emergency stops and increasing the number of successful journeys. On the other hand, the ageing process over time leads to quality loss because of environmental aspects and chemical reactions inside the materials. Thus, the conditions of structures have to be updated in terms of quality. Meanwhile, routine inspections play a crucial role in maintenance activities [1].  
As a first step, gathering data is necessary for a researcher in the field of quality control. Based on intermittent train movements with short intervals, inspections between operations are necessary for safety. However, many crises threaten inspectors during the operation if they follow traditional inspection methods. Defect growth estimation denoted by automatic monitoring methods through various sorts of sensors prepares a database for finding the durability index. In this manner, structural health monitoring (SHM) methods update a structural component's state based on the prepared database [2]. The SHM utilizes various methods, such as damages and cracks detection using images, vibration indications, and abnormal measurement diagnosis [3]. In this manner, several sensors (for factors such as humidity, temperature, pressure, load, potential hydrogen (PH), hardness, chemical composition, acoustic emission (AE), and others) have been employed for data gathering and damage detection [4; 5]. AE was used for damage monitoring in reinforced concrete structures [6; 7]. In this way, AE is applied to highlight internal damage progress [8]. However, the complex fracture and defects cannot be efficiently categorized and rated by measuring deterioration with certain monitoring methods, such as visual sensors, but these sensors can detect surface defects and mechanical features [8]. One of the famous tests for examining concrete elements is a visual inspection which uncovers damages, and automated visual inspections measure the defect growth. Since real-time monitoring is necessary for reliability assessment, automated visualization is an essential tool for inspectors to evaluate objects without human error, combined with regular inspections.  
Analyzing data in automation systems has been carried out by researchers using intelligent approaches [9]. There are three levels desirable for SHM during operations. The first level focuses on defect detection and alarms for the operator. The second level prepares a baseline for comparing elements and their priority. The third level guides decision makers to estimate and forecast future demands through trends. In this manner, the semantic web layer acts as the \"brain\" to provide the basis for determining defects [10]. However, this research presents risk as a basic factor for automation in decision-making after image processing and data gathering.  
Electrical conductivity is an aspect in terms of structural health monitoring methods which can estimate the durability of reinforced concrete [11]. However, the durability of reinforced concrete after risk analysis is not connected with image processing based on the surface degradation status within a specific time window. This research will focus on this gap to improve condition-based maintenance.