PSI - Issue 62

Andrea Meoni et al. / Procedia Structural Integrity 62 (2024) 73–80 Meoni et al/ Structural Integrity Procedia 00 (2019) 000 – 000

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3.3. Structural performance evaluation

The case study bridge was recently visually inspected by some of the Authors to assess its structural defectiveness. Given the height of the piers, a by-bridge platform was adopted to properly inspect the bridge superstructure. Defects revealed during the inspection were noted in digital worksheets and then uploaded to the adopted BIM platform. Subsequently, the defect level of the case study bridge was evaluated according to the provisions of the Italian Guidelines through the implementation of the evaluation procedure in the BIM platform by using customized Python scripts. A photogrammetric survey of the case study bridge was also carried out during visual inspection. To do that, a professional drone, model DJI Mavic 3 Enterprise series, equipped with a 20MP camera and a 4/3 CMOS sensor, was used to scan the structure from different points of view. The resulting point cloud was first cleaned via the Metashape Pro software, then imported into the adopted BIM platform and overlaid on the digital 3D model of the bridge. The point cloud can then be used as a reference for future defect monitoring activities. Ambient Vibration Tests (AVTs) were carried out on the case study bridge on October 19th, 2022, hence Operational Modal Analysis (OMA) was used to determine the modal features characterizing the dynamic response of the structure. These can be considered as reference modal features for future SHM activities. In fact, as it is well known, possible changes in modal features over time can be indicative of modifications in the global structural performance of a structure due to the occurrence of damage (Brincker et al 2001). Figure 3 illustrates the measurement setup adopted to test span no. 8 and half of span no. 7 and 9. Each measurement station consisted of a high-sensitivity (10 V/g) uniaxial accelerometer, model PCB393B12, oriented along the z-axis by using stabilizing steel support deployed on the concrete curbs of the deck of the bridge. The data acquisition system was a NI cDAQ 9188, equipped with four NI 9234 modules for dynamic signal acquisition. The duration of each acceleration measurement was 35 minutes, while the sampling rate was set to 1653 Hz. Acceleration signals were detrended and resampled at 40 Hz before using the Frequency Domain Decomposition (FDD) technique to extract the modal features of the bridge. Processing operations were carried out directly within the BIM platform by using custom Python scripts to implement the dynamic identification algorithm.

Fig. 3. Measurement setup adopted to perform ambient vibration tests on the case study bridge.

4. Results The BIM-based approach for informed management of bridges proposed in Section 2 was implemented in the Revit BIM platform through the development of “BridgeBIM”, a n add-in application based on custom Python scripts, powered by the pyRevit plugin (Iran-Nejad 2020), and accessible via the Revit ribbon panel. Figure 4 shows the digital 3D model of the case study bridge together with the main panel of BridgeBIM. 4.1. Module for risk evaluation Figure 5(a) illustrates the main panel of the Risk Evaluation Module. The extent of every risk condition affecting the bridge under evaluation was assessed by considering the five risk levels provided by the Italian Guidelines, namely high, medium-high, medium, medium-low, and low risk. Figures 5(b) and 5(c) show exemplifications of a worksheet collecting defects detected on a structural element of the case study bridge during the performed visual inspection (defect level is a key parameter for the evaluation of structural and seismic risk conditions) and of a worksheet gathering information required in the assessment procedures of the diverse risk conditions encompassed