Comparative analysis of approaches for automated compliance checking of construction data

• Published in: Advanced engineering informatics Vol. 60; p. 102443
• Main Authors: Nuyts, Emma, Bonduel, Mathias, Verstraeten, Ruben
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01.04.2024
• Subjects: Automated compliance checking; Building information management; Comparative analysis; Building information management; Automated compliance checking; Comparative analysis
• ISSN: 1474-0346; 1873-5320
• DOI: 10.1016/j.aei.2024.102443

While the domain of Automated Compliance Checking (ACC) has gained track, the construction industry has been flooded with different approaches. This paper studies these different approaches for use in compliance checking of construction data. The approaches are compared by defining constraints for the same set of five requirements, each of a different category, stemming from the Flemish building regulation on accessibility. Eight approaches have been selected for comparison: two IFC-based approaches (Solibri Model Checker and the upcoming buildingSMART standard IDS), two general data standards and their accompanying schema definition languages (JSON Schema and XSD), and four Linked Data approaches (OWL, SWRL, SPARQL, and SHACL). Besides the pure functional analysis, the relative uptake and support in tooling are also considered. While XML/XSD and JSON/JSON Schema and the Linked Data approaches are in essence domain-independent, only the latter has an extra layer for agreeing on high-level data modeling (and thus data validation) patterns in the construction domain with the EN17632-1:2022 standard. SHACL is considered the most adept method from the Linked Data approaches since it is fully standardized for both inputs and outputs and was developed for validation use cases. •The Flemish regulation on accessibility exemplifies five constraint categories.•Eight compliance checking approaches are compared by executing the five constraints.•The (dis)advantages of each approach are explained.•Sharing all constraints, datasets, outputs, and code, ensures full reproducibility.