## 1 Introduction
### Bridge Inspection  
The American Association of State Highway and Transportation Officials (AASHTO) Manual for Condition Evaluation of Bridges, 1994 describes five types of bridge inspection [24]. These are: (i) initial inspection, (ii) routine inspection, (iii) in-depth inspection, (iv) damage inspection and (v) special inspection. Initial inspection with lower cost support by visual inspection can be performed at several levels of detail, from rapid ones to very detailed ones when all the elements of the structure are analyzed [25; 26]. For the first step in this research, input data were obtained by initial inspections and visual tests, as well as questionnaire forms. The results were obtained after analyzing the raw dataset feeding posterior decision support system for planning routine, in-depth and special inspections. Categorizing the defects of bridge concrete elements after inspection remains a subjective and laborious task. This is important in light of over-aging bridge stock for avoiding destructive bridge collapses, as happened in 2018 in Genoa, Italy [27]. For routine inspection, the frequency of inspection depends on the condition state and local features and defects. This study aims at providing a framework for decision-making to find adequate inspection intervals and valuable data. For in-depth inspection, it is necessary to plan and perform non-destructive tests (NDT). Therefore, this process will monitor, probe and measure the material performance based on these tests. The measured response relies on the analyzed material property and the test object itself. Examples of the NDT method for bridges are visual inspection, ultrasound tests, thermography, and electrical resistivity, among others. In this sense, the proposed framework is a tool for optimizing the planning of inspections and tests that are considered important in attending to the property or element to be analyzed. Condition monitoring is used as a framework to derive information for reliability calculation. In this case, irregularities and damage detection are variables to take into account within the RCM concept. For instance, recent research has attempted to detect structural features and analysis of their behavior to measure asset performance during operation [28; 29; 30]. According to the type of material and the consequences of certain damage, the type of inspection is selected for each element [31]. For instance, damages make a change in the structural behavior of the element (e.g., change in stiffness) that can only be analyzed by specific types of inspections. Based on this, measuring the change in the performance of a given parameter (e.g., loss of stiffness) indicates that the structure may be affected by specific damage and with that knowledge it is possible to prepare a database for RCM [32]. In recent research, an attempt to evaluate the relationship between track damages and stiffness changes was made in [33]. Numerical simulation is another method that is being used for sensitivity analysis in railway track stiffness and structural behavior prediction [34; 35]. Rail surface damage detection is another subject that is considered in recent research for RCM based on their damage type [36; 37; 38]. After monitoring and damage detection, the process of RCM is updated with information that has been extracted within the SHM. Therefore, to obtain the reliability index, it is necessary to consider the probability of occurrence and the defect consequences based on their location and other technical features of the structure. Additionally, FEM (Finite Element Method) has been applied for finding models which are useful for predicting exposures and consequences of a defect in the future. This model has to be calibrated and validated by experimental data through a comparison of the real situation (onsite testing and inspection) and the FEM model. Prioritization is one of the most important tools for optimizing decision making. To make a decision without wasting resources, it is necessary to act based on the conditions.