## 3 Research Methodology
To ensure interoperability and efficient management of construction data, the research uses the IFC schema not only as a standardized data format but as a structured information model that improves access to both cost and energy-related data. Unlike traditional BIM models that often store cost and sustainability data as static, non-queryable attributes, this research ensures that such information is represented as structured data objects within the IFC schema, enabling efficient search, access, and analysis [34]. By structuring cost and energy information as structured data objects, stakeholders can efficiently retrieve, analyze,and compare different retrofit scenarios. This dynamic IFC integration enhances decision-making transparency, allowing users to explore cost-energy trade-offs without relying on external tools or manual data extraction. To implement this integration in practice, the methodology follows two structured processes:  
* Structured Cost Dataset Development, A structured cost dataset was developed to represent the 12 retrofit scenarios, where each building object is associated with 12 distinct cost items, each corresponding to a scenario. These cost items are defined using the IfcCostItem entity and are quantitatively derived from the object's geometry. The cost items are grouped into separate cost sheets (one per scenario), representing alternative cost configurations for each retrofitting option. These cost data, sourced from the Regione Lombardia price list, are organized according to a domain-specific ontology previously developed by the research group [34], enabling standardized integration within the IFC schema. The ontology classifies cost items by element type, function, and material properties, enabling consistent BIM integration. Its standardized structure supports reusability across different regional databases and retrofit projects. This structure ensures consistent, interoperable, and queryable cost information within the BIM environment, facilitating comparison across all retrofit scenarios. * Semi-automated Cost-Element Linkage, To support interoperability and consistency, Python 3.13.4 scripting combined with the IfcOpenShell library is used to partially automate the linkage between IFC elements and cost items [28]. This process extracts structured data from each geometric object--such as classification, dimensions, and material type--and uses it to filter relevant IfcCostItem entries from the dataset. While the script reduces the number of possible matches, the final selection of the appropriate cost item is completed manually by the user. This semi-automated workflow enhances the efficiency and reliability of the cost-assignment process while ensuring that cost data remain linked to the thermal and physical attributes of building components. Although predefined cost values were used in this study, the framework is designed to support dynamic price updates. By extending the existing Python and IfcOpenShell-based workflow, updated prices from structured sources can be programmatically imported and mapped to corresponding IfcCostItem entities. This enhancement would improve the model's responsiveness to market fluctuations while maintaining interoperability within the IFC structure. * **Phase 3--Identification of Optimal Retrofitting Scenario Through Energy-Cost Integration:** This phase, as part of the post-retrofit analysis, focuses on the comparison of multiple energy retrofitting scenarios by integrating energy performance and associated costs into a unified evaluation framework. Using the total cost as the sole evaluation metric, this phase enables the identification of the optimal retrofitting scenario that balances energy efficiency and economic feasibility. The cost data integrated with the energy analysis report, embedded into the BIM model during Phase 2, enables the evaluation of trade-offs among material, installation, and operational energy costs. By focusing on the total cost of each scenario, the framework provides a clear and quantitative basis for decision-making.