IFC-based automated deconstructability analysis with market data integration through knowledge graphs

Deconstruction is vital for circular economy implementation in the construction sector, yet thin profit margins stifle its large-scale adoption. Consequently, deconstruction demands rigorous and data-driven planning. While Building Information Modeling (BIM) can serve this purpose, its readiness for deconstruction remains limited. This paper introduces DeconPlanner , a system that integrates Knowledge Graphs (KGs) and Industry Foundation Classes (IFC) to support digitally enabled deconstruction planning through two modules: an inference-based module that semantically enriches BIM with deconstruction sequencing; and an AI-powered market integration module that semantically enhances BIM by establishing semantic links between facility inventory and potential downstream stakeholders. Validation through a real-world case study demonstrated high performance for dependency inference (96.6% precision; 97.3% recall) and market integration (99.4% precision; 94% recall). Additionally, expert interviews confirmed the system's practicality and industry relevance. The findings demonstrate this framework's potential to reduce information fragmentation and establish the semantic foundation for future performance-based deconstruction workflows.

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Publication Details

Journal
Automation in Construction
Published
2026-08-26
DOI
https://doi.org/10.1016/j.autcon.2026.107219
Primary Topic
Manufacturing Process and Optimization
Type
article
Field-Weighted Citation Impact
0.00

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article

IFC-based automated deconstructability analysis with market data integration through knowledge graphs

Mazdak Nik-Bakht, Amr S. Allam
Automation in Construction
Manufacturing Process and Optimization
article

IFC-based automated deconstructability analysis with market data integration through knowledge graphs

Mazdak Nik-Bakht, Amr S. Allam
article en

Abstract

Deconstruction is vital for circular economy implementation in the construction sector, yet thin profit margins stifle its large-scale adoption. Consequently, deconstruction demands rigorous and data-driven planning. While Building Information Modeling (BIM) can serve this purpose, its readiness for deconstruction remains limited. This paper introduces DeconPlanner , a system that integrates Knowledge Graphs (KGs) and Industry Foundation Classes (IFC) to support digitally enabled deconstruction planning through two modules: an inference-based module that semantically enriches BIM with deconstruction sequencing; and an AI-powered market integration module that semantically enhances BIM by establishing semantic links between facility inventory and potential downstream stakeholders. Validation through a real-world case study demonstrated high performance for dependency inference (96.6% precision; 97.3% recall) and market integration (99.4% precision; 94% recall). Additionally, expert interviews confirmed the system's practicality and industry relevance. The findings demonstrate this framework's potential to reduce information fragmentation and establish the semantic foundation for future performance-based deconstruction workflows.

Automation in ConstructionVol. 192
Concordia University (CA)
National Research Council Canada, Fonds de recherche du Québec – Nature et technologies
Industry, innovation and infrastructure
Openalex Percentile: Top 10%
Manufacturing Process and Optimization
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