PSI - Issue 64

Lucas Martins Barreto et al. / Procedia Structural Integrity 64 (2024) 1168–1175 Author name / Structural Integrity Procedia 00 (2019) 000 – 000

1174

7

management. From the sensors, the data to be implemented in the Digital Twin are extracted, in this phase the information related to damage and the scenario of possible damage are evaluated and managed. Values referring to the structural conditioning of the bridge are also obtained, which, against pre-defined values, can be calculated possible failures through probabilistic calculations. Among the large number of different data from different sensors, a system was adopted to define a qualitative analysis of the bridge structure by means of partial condition indicators (IPC), having as the numerical value 1.0 as a part in the best condition and 4.0 as a part with compromised structural function. In addition to the qualitative measurement, the installed sensors generate data related to the tension observed in the steel cables, movement of the structural elements and the overload along the bridge. In this way, several data from PCIs converge to analyze a structural set, unifying them and are called condition indicators (CI). According to Herbrand et al (2022), the fusion of the information obtained through on-site inspections are executed within a period of 3 to 6 years, with the real-time monitoring data coming from the sensors. The merge of these data is carried out through the SIB-Bauwerke software, in which the insertion of the data obtained in the field is compatible in a qualitative way for a numerical factor. With the acquisition of all the data and its compatibility, it is necessary to create the digital model that will serve as the basis for the Digital Twin. In this study, its creation was developed from LiDAR (Light Detection and Ranging) technology, which enables through the capture of images a volumetric composition of a solid, thus creating in a BIM system a structure for the Digital Twin.With the basis for the Digital Twin ready and all data compatible, all this information was stored in a unified database on the Open Geospatial Consortium (OGC) Sensorthings server. This software enables the interconnection of IoT (Internet of Things) devices, data, and applications over the Web. The data was then processed following the Extract-Transform-Load (ETL) methodology, where small processes are chained together by logical systems to aggregate different data into one piece of information (GRABE et al, 2020). After obtaining, processing, and grouping the data, the whole process for calculating the IPCs and CIs is made possible, in this process different data under different conditions are combined to obtain the desired information. And finally, for the visualization of these, an intuitive visual interface was developed for the manipulation of this information, this software was developed with the combination of WebGL and html5 systems as well as augmented reality or virtual reality (VR) devices (WENNER et al, 2021). 6. Conclusion In conclusion, this study has provided a thorough examination of the application of Digital Twins in the construction, maintenance, and monitoring of reinforced and prestressed concrete bridges. Through a review of recent literature and the inclusion of relevant case studies, it is possible to understand how Digital Twins can revolutionize bridge engineering. By focusing on the structural health monitoring aspect, it was identified key opportunities for enhancing bridge maintenance practices. Digital Twins are not just a graphical representation, like images, fed with data they can simulate how structural elements are behaving and how they might behave on different occasions. As mentioned in item 3, this technological approach could be applied to monitor the structural health of a bridge, both in reinforced and prestressed concrete, collecting data in the field of the main loads, and pathological manifestations developed over time on these structures. The case study shows in detail the entire procedure from beginning to end the process of creating their respective Digital Twins. As the greatest difficulties observed by the authors, we can highlight the large volume of data with different origins, making the integration process between them complex. There is also the difficulty of communication between some software, causing limitations or the need for software development, as observed in item 5. The installation of several sensors, acquisition of software, and hiring of specialized labor for constructing a Digital Twin incur high costs. However, these expenses are outweighed by the potential savings in maintenance costs should pathologies progress to severe structural damage. Moreover, the human toll of such structural failures, which cannot be quantified monetarily, underscores the importance of preventive measures to avoid accidents resulting in loss of assets and lives. Thus, we can conclude that Digital Twins are already being a great step in improving the monitoring of the structural health of reinforced and prestressed concrete bridges, they still need improvements in technological aspects,