# HBIM: Background, Current Trends, and Future Prospects
## 5 Applications and Data Management
### HBIM for Structural Analysis  
Although the idea of linking HBIM models with structural analysis models has been considered almost from its beginnings [24; 25; 98], the interest in this possibility has grown as HBIM has gained popularity and solidified its role as a comprehensive building management tool. The most common way to integrate structural analysis into an HBIM environment involves developing a 3D model in BIM software using the data collected from the cultural heritage building. From this complex HBIM model, a simplified geometry is extracted and imported into analysis software, typically finite element analysis (FEA) software, where the necessary calculations are performed [62; 99]. The results from these analyses should then be incorporated back into the HBIM model, updating it accordingly. This type of workflow was followed by [PERSON] et al. [100], who proposed a methodology that began with the development of an HBIM model in Revit, incorporating data from a historical survey, laser scanner measurements, and local NDT. The model was then exported to the FEA software Midas in a _.sat_ or IFC (Industry Foundation Class) format, where it was meshed, and modal and pushover analyses were conducted. All steps, i.e., exportting, meshing, and analysis, had to be done manually. A similar methodology was proposed in the project \"CHARMING PISTOIA\" [101] for the preservation and maintenance of heritage structures, although the application was limited to a single architectural element. Alternatively, [PERSON] et al. [102] presented a different approach, starting with the creation of a parametric model in Rhino-Grasshopper, which could be exported to the SAP2000 FEA software or to an HBIM digital archive using specific plug-ins and the IFC format. This workflow allowed them to model and analyze 3D shells, but there was no integration or data exchange between the structural analysis software and the final HBIM model. The interaction between Rhino-Grasshopper and FEA software, Midas, in this case, was also successfully explored by [PERSON] et al. [103] using a scan-to-BIM procedure to build both an HBIM and a structural model from a 3D point cloud. In the last few years, there has been a growing interest regarding the possibility of constantly updating HBIM models with real-time data about the structural condition of the building as a powerful tool for preventive conservation and heritage management. The goal is to use the information measured on-site through a continuous structural health monitoring (SHM) system to analyze the structural behavior of the CH asset and then store and update this information in the HBIM model, doing all this in a continuous automated process [97; 104; 105]. While finite element models can be used to assess specific structural aspects or evaluate particular load conditions, their complexity and high computational demands often render them impractical for real-time SHM schemes. As a result, damage identification frequently requires the development of computationally efficient surrogate models, such as physics-based or machine-learning approaches, which utilize continuous  
Figure 8: Percentage of HBIM publications by type of building (**a**) and construction material (**b**) of the heritage asset evaluated. monitoring data to infer and classify the building's health condition, effectively functioning as a structural digital twin [106]. Digital twins can enhance HBIM models by capturing dynamic data from the physical heritage asset, enabling updates to the virtual model and providing predictive insights [107], but there are still unresolved challenges related to the interoperability between HBIM and digital twins used for continuous structural performance analyses [97; 108]:  
* Developing 3D models for heritage structures with intricate geometric features. * Ensuring the right level of detail and accuracy in the digital model to capture typical heritage elements, deterioration patterns, and cracks while balancing costs, time, and results. * Interoperability between tools and datasets.