PSI - Issue 62

Luigi Pallante et al. / Procedia Structural Integrity 62 (2024) 268–275 Pallante L. et al. / Structural Integrity Procedia 00 (2019) 000 – 000

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The MIT guidelines provide the appropriate calculation procedures that are used to determine the classification of bridge attention classes. In accordance with the standards, the attention classes are classified into five ascending levels, ranging from "LOW" to "HIGH" (indicating higher risk). Each bridge in the GIS environment can have its corresponding attention classes assigned to it, and the filtered data can be shown at the regional level. As previously mentioned, the corresponding color scale, which goes from light green (LOW level) to red (HIGH level), represents the attention class levels. It is also feasible to display and isolate bridges that satisfy specific requirements or criteria. Furthermore, the GIS environment records and maintains information regarding the most current visual inspections that were carried out on the bridges. Additionally, the GIS environment has the data related to the most recent visual inspections of the bridges. As in the GIS environment, the same classification is also used for the BIM environment to have uniformity between the different systems. The only difference between the GIS and BIM representation is that the first one aims to show a comparison between the different attention classes of the bridges in a network context, while the second one is focused on showing the defect level of the various elements of the individual bridge. Thanks to this criterion, it is possible to easily display the model and the various elements of the structure with the colour associated with the defect level established through the calculations performed. The necessary strings are entered to describe the attributes of the defect level of the elements. In order to visualize the defect level, a customized parameter must be added to the BIM element that makes up the affected portion of the bridgework. After that, the calculated defect level value stored in the database must be automatically assigned, linking the visual information of the defect level with the defect level, as shown in Figure 5..

Fig. 5.Defect level visualized in BIM environment

5. Conclusions The research introduces a comprehensive, data-driven approach to bridge safety management in the Lazio region. The approach uses parametric Building Information Modeling (BIM) models, a Geographic Information System (GIS), and a structured database in accordance with MIT principles. The foundation is the structured database, which makes risk analysis and ongoing monitoring easier. Bridge assets are shown and georeferenced using GIS, which also offers a regional overview with attention class indicators. Bridge elements are dynamically represented by parametric BIM models, which integrate defect levels for focused maintenance. The starting point is the structured database, which makes risk analysis and ongoing monitoring easier. Bridge assets are shown and georeferenced using GIS, which also offers a regional overview with attention class indicators. Bridge elements are dynamically represented by parametric BIM models, which integrate defect levels for focused maintenance. Findings show how to apply the technology practically and provide infrastructure managers with tools for making educated decisions on the safety and upkeep of current bridges. By proposing an integrated strategy that makes use of digital technologies and data-driven methodologies, this research advances bridge management systems.