André Borrmann — Page 2

arXiv2023

CBIM: A Graph-based Approach to Enhance Interoperability Using Semantic Enrichment

Interoperability remains a challenge in the construction industry. In this study, we propose a semantic enrichment approach to construct BIM knowledge graphs from pure building object geometries and demonstrate its potential to support BIM interoperability. Our approach involves machine learning and rule-based methods for object classification, relationship determination (e.g., hosting and adjacent) and attribute computation. The enriched results are compiled into a BIM graph. A case study was conducted to illustrate the approach for facilitating interoperability between different versions of the BIM authoring software Autodesk Revit. First, pure object geometries of an architectural apartment model were exported from Revit 2023 and fed into the developed tools in sequence to generate a BIM graph. Then, essential information was extracted from the graph and used to reconstruct an architectural model in the version 2022 of Revit. Upon examination, the reconstructed model was consistent with the original one. The success of this experiment demonstrates the feasibility of generating a BIM graph from object geometries and utilizing it to support interoperability.

cs.SE↗

arXiv2022

Introducing causal inference in the energy-efficient building design process

"What-if" questions are intuitively generated and commonly asked during the design process. Engineers and architects need to inherently conduct design decisions, progressing from one phase to another. They either use empirical domain experience, simulations, or data-driven methods to acquire consequential feedback. We take an example from an interdisciplinary domain of energy-efficient building design to argue that the current methods for decision support have limitations or deficiencies in four aspects: parametric independency identification, gaps in integrating knowledge-based and data-driven approaches, less explicit model interpretation, and ambiguous decision support boundaries. In this study, we first clarify the nature of dynamic experience in individuals and constant principal knowledge in design. Subsequently, we introduce causal inference into the domain. A four-step process is proposed to discover and analyze parametric dependencies in a mathematically rigorous and computationally efficient manner by identifying the causal diagram with interventions. The causal diagram provides a nexus for integrating domain knowledge with data-driven methods, providing interpretability and testability against the domain experience within the design space. Extracting causal structures from the data is close to the nature design reasoning process. As an illustration, we applied the properties of the proposed estimators through simulations. The paper concludes with a feasibility study demonstrating the proposed framework's realization.

stat.ME↗