BIM And GIS In Predictive Maintenance of Tunnel

BIM (Building Information Modeling) and GIS (Geographic Information Systems) are increasingly important tools in modern engineering, especially when it comes to predictive maintenance. In this thesis, we explore how combining these technologies can improve the way we manage and maintain infrastructure, focusing on the case study of the Monreale Tunnel (Galleria A7). We got a 3D BIM model of the tunnel by using Autodesk Revit. From there, we tested how well the model could integrate with various GIS and BIM tools, including InfraWorks, Blender, Cesium Ion, ArcGIS pro. Each software brought unique capabilities that helped us collect more accurate and detailed data about the tunnel and its surrounding environment. This process revealed the strengths of BIM-GIS integration in bringing real-world context into predictive maintenance planning. However, the journey wasn’t without its challenges. One of the main issues we faced was georeferencing ensuring that data from different software aligned correctly in real-world coordinates. To tackle this, we relied on IFC (Industry Foundation Classes) to standardize data exchange and improve compatibility between platforms. Additionally, we used ArcGIS pro to create geological maps, such as slope maps, hillshades, and aspect maps, to better understand the tunnel’s surroundings. These maps provided valuable insights and set the stage for discussing how the integration of BIM and GIS could support future development and maintenance efforts. This work highlights the potential of BIM and GIS to revolutionize predictive maintenance, showing not only their benefits but also the challenges that come with integrating such advanced tools. By addressing these challenges, we aim to contribute to more efficient and informed infrastructure management in the future.