Capabilities of rule representations for automated compliance checking in healthcare buildings

Abstract

A suitable rule representation is essential to enable automated compliance checking of building design. It encapsulates engineering knowledge and facilitates an adequate interpretation of design standards. However, existing methods have achieved limited capabilities to represent rules for automated compliance checking. Thus, they merely worked for limited types of rules. This paper aims to identify capabilities needed for rule representation by using healthcare design regulations as an example. It can serve as a foundation for developing rule engines and compliance-checking systems in the future. A four-step process was used to systematically analyse six healthcare building regulations in rule-oriented and implementation aspects. The results showed 18 capabilities for healthcare rule representation, where 16 are required, and two are desirable. This research is valuable to researchers and practitioners by providing a checklist for future representation development and criteria for assessing rule representation methods.

Publication DOI: https://doi.org/10.1016/j.autcon.2022.104688
Divisions: College of Engineering & Physical Sciences > School of Infrastructure and Sustainable Engineering > Civil Engineering
College of Engineering & Physical Sciences
Additional Information: Copyright © 2022 The Authors. Published by Elsevier B.V. This is an open access article under the CCBY license (http://creativecommons.org/licenses/by/4.0/).
Uncontrolled Keywords: Automated compliance checking,Building regulations,Healthcare building design,Knowledge representation,Control and Systems Engineering,Building and Construction,Civil and Structural Engineering
Publication ISSN: 0926-5805
Last Modified: 29 Feb 2024 08:22
Date Deposited: 19 Jul 2023 08:30
Full Text Link:
Related URLs: https://www.sci ... 5581?via%3Dihub (Publisher URL)
PURE Output Type: Article
Published Date: 2023-02-01
Published Online Date: 2022-11-30
Accepted Date: 2022-11-22
Authors: Zhang, Zijing (ORCID Profile 0000-0003-0332-5276)
Nisbet, Nicholas
Ma, Ling
Broyd, Tim

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