Computerising the New Zealand Building Code for automated compliance audit
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Authors
Dimyadi, J.
Fernando, S.
Davies, Kath
Amor, R.
Fernando, S.
Davies, Kath
Amor, R.
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Date
2020-02-20
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Type
Conference Contribution - Paper in Published Proceedings
Ngā Upoko Tukutuku (Māori subject headings)
Keyword
New Zealand
New Zealand Building Code
automated compliance audit
BIM (building information modelling)
LegalRuleML
RuleML
computable rules
audit quality
compliance
business rules engines
New Zealand Building Code
automated compliance audit
BIM (building information modelling)
LegalRuleML
RuleML
computable rules
audit quality
compliance
business rules engines
ANZSRC Field of Research Code (2020)
Citation
Dimyadi, J., Fernando, S., Davies, K., & Amor, R. (2020). Computerising the New Zealand Building Code for Automated Compliance Audit. In Wajiha Mohsin Shahzad, Eziaku Onyeizu Rasheed, James Olabode Bamidele Rotimi (Ed.), Proceedings – New Zealand Built Environment Research Symposium , Vol. 6 (pp. 39-46). Retrieved from http://nzbers.massey.ac.nz/wp-content/uploads/2020/03/Proceedings-NZBERS-Feb2020.pdf
Abstract
One key ingredient in the automated compliance audit process is the availability of a computable form of normative requirements (e.g. codes and standards), which are usually written in natural language intended for human interpretation and not readily processable by machines. The predominantly ‘Blackbox’ approach of hardcoding these computable normative rules into a compliance audit system has been reported to be problematic and costly to maintain in response to frequent regulatory changes. The current research sets out to investigate to what extent normative texts can be represented as computable rules for automated compliance audit as well as to ease maintenance in response to changes in the source documents. A set of priority compliance documents supporting the New Zealand Building Code has been selected as the subject for a case study. This paper describes the digitisation and quality assurance process, the knowledge extraction experience, and challenges identified during the study. Furthermore, the paper explores how the legal knowledge captured by the digitised rules can be used effectively in an automated compliance audit environment. The findings from the study suggest that a semi-automated digitisation process is feasible and up to 80% of prescriptive text can be translated and encoded into the open standard LegalRuleML. However, only approximately 50% of these can be used directly in an automated compliance audit environment without any human intervention. The lessons learnt from the study can be used towards improving the digitisation process. Ultimately, this could in turn help to improve the natural language source text in subsequent revisions of the codes.
Publisher
New Zealand Built Environment Research Symposium (NZBERS)
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