PSI - Issue 84

Adriana Marra et al. / Procedia Structural Integrity 84 (2026) 661–668

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modeling process and reorganize the model's information structure for integration with the database and subsequent sharing in interoperable information systems. Following the optimization of the model's individual components, the Mark parameter, defined as the component's unique identifier, was manually entered (see Fig. 4). This value was made consistent with the Asset Manager Component ID (local) used in the database tables, ensuring a one-to-one correspondence between the two information structures. The alignment between these two systems is fundamental for enabling semi-automated update flows to reliably and traceably transfer information from the database to the BrIM model, ensuring a consistent and replicable process.

Fig. 4. BrIM model optimization: updating IFC parameters and entering the unique code in the Mark parameter.

3.3. BrIM model semi-automated data refresh

The final phase of the workflow involves the application of two scripts developed in Dynamo to semi-automatically transfer the data from the database to the BrIM model, and to support the parametric assessment of the element defect level and the structural and foundation risk of the structure. The first graph operates on the data contained in the Inspection form, enabling the association between information collected during visual inspections with each model's element, in line with level 1 of the Guidelines' multi-level approach. The process, which can be managed in Revit through the Dynamo Player, activates the routine for periodic information refresh within the modeling environment, selecting only two input elements: the reference to the CSV file exported from the Inspection form and the modeled element. After providing the inputs, the graph automatically analyzes the imported CSV data and runs a process to verify and filter the information. Specifically, it compares the list of Asset Manager Component ID (local) identifiers with the Mark value of the selected element to isolate the information associated with the object. The graph checks for the presence of one or more inspections, generating a warning if the inspection date is not unique and indicating the required next steps (Marra et al 2025). Subsequent nodes then dynamically generate the necessary parameters, in a variable number depending on the number of defects detected, automatically associating the corresponding values (e.g., InspectionDate, DefectCode, DefectDescription, weight G, extension K1, intensity K2, etc.), avoiding any manual operation. Finally, the graph uses a custom Codeblock to calculate the element's defect level. This code block combines the G, K1, and K2 values with information on the element's static and structural characteristics, in accordance with the guidelines. The result is the defect level class, which is associated with a specific parameter and color coding, so that the condition of the element to be immediately highlighted and displayed (see Fig. 5). On the other hand, the second script operates on the information collected in the Level 0 Census form to semi automatically assess the asset's structural and foundational AC. After importing the CSV file exported from the census form, Dynamo's native nodes analyze and process the records corresponding to the primary and secondary parameters to define the hazard, vulnerability, and exposure indices. A series of custom Codeblocks then combines the different