Work

Automated and scalable BIM2BEM framework with zoning-based model simplification leveraging knowledge graph integration

Year: 2025

Type: article

Abstract: Accurate and scalable generation of Building Energy Models (BEM) from Building Information Modelling (BIM) data is critical for performance-driven building design. However, existing methods are often constrained by data quality issues and rigid workflows, limiting automation. This paper proposes an automated and scalable BIM-to-BEM (BIM2BEM) framework enabled by knowledge graph integration, designed to support automation and scalability in model generation from imperfect BIM data. To manage model complexity, zoning-based mappings from BIM spaces to thermal zones are derived through multi-factor analysis of spatial relationships, functional usage, thermal load similarity, and HVAC configuration. Applied to a real-world complex building, the framework reduces simulation time by up to 70%, while maintaining energy use deviations within 3% and HVAC sizing variations up to 10%, compared with the full-model baseline. These findings indicate that the proposed framework can enhance BIM2BEM automation, supporting the scalable and flexible generation of simulation-ready models under practical data limitations.

Source: Automation in Construction

DOI: 10.1016/j.autcon.2025.106712

Authors: Meng Wang, Georgios N. Lilis, Dimitris Mavrokapnidis, Kyriakos Katsigarakis, Ivan Korolija, Dimitrios Rovas

Institution: University College London

Language: English

Topic: Building Energy and Comfort Optimization

Subfield: Building and Construction

Field: Engineering

Domain: Physical Sciences

SDGs: Affordable and clean energy, Industry, innovation and infrastructure, Sustainable cities and communities

Open Access status: hybrid

Estimated APC paid: $5,490