A Fully Automated DM-BIM-BEM Pipeline Enabling Graph-Based Intelligence, Interoperability, and Performance-Driven Early Design

TL;DR

This paper introduces an automated framework that converts early-stage building designs from unstructured geometry into knowledge graphs and energy models, facilitating AI-driven design analysis and optimization.

Contribution

It presents a novel, fully automated pipeline that transforms B-rep geometry into knowledge graphs and energy models, enabling explicit semantic and performance understanding in early design stages.

Findings

High robustness across diverse datasets

Accurate topological reconstruction

Reliable performance-model generation

Abstract

Artificial intelligence in construction increasingly depends on structured representations such as Building Information Models and knowledge graphs, yet early-stage building designs are predominantly created as flexible boundary-representation (B-rep) models that lack explicit spatial, semantic, and performance structure. This paper presents a robust, fully automated framework that transforms unstructured B-rep geometry into knowledge-graph-based Building Information Models and further into executable Building Energy Models. The framework enables artificial intelligence to explicitly interpret building elements, spatial topology, and their associated thermal and performance attributes. It integrates automated geometry cleansing, multiple auto space-generation strategies, graph-based extraction of space and element topology, ontology-aligned knowledge modeling, and reversible transformation between ontology-based BIM and EnergyPlus energy models. Validation on parametric, sketch-based, and real-world building datasets demonstrates high robustness, consistent topological reconstruction, and reliable performance-model generation. By bridging design models, BIM, and BEM, the framework provides an AI-oriented infrastructure that extends BIM- and graph-based intelligence pipelines to flexible early-stage design geometry, enabling performance-driven design exploration and optimization by learning-based methods.