Integrating semantic NLP and logic reasoning into a unified system for fully-automated code checking
Existing automated compliance checking (ACC) systems are limited in their automation; they rely on the use of hard-coded, proprietary rules for representing regulatory requirements, which requires major manual effort in extracting regulatory information from textual regulatory documents and coding t...
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Published in | Automation in construction Vol. 73; pp. 45 - 57 |
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Main Authors | , |
Format | Journal Article |
Language | English |
Published |
Amsterdam
Elsevier B.V
01.01.2017
Elsevier BV |
Subjects | |
Online Access | Get full text |
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Summary: | Existing automated compliance checking (ACC) systems are limited in their automation; they rely on the use of hard-coded, proprietary rules for representing regulatory requirements, which requires major manual effort in extracting regulatory information from textual regulatory documents and coding these information into a rule format. To address this limitation, this paper proposes a new unified ACC system that integrates: (1) semantic natural language processing techniques and EXPRESS data-based techniques to automatically extract and transform both regulatory information (in regulatory documents) and design information [in building information models (BIMs)] for automated compliance reasoning, and (2) semantic logic-based information representation so that the reasoning could be fully automated. To test the proposed system, a BIM test case was checked for compliance with Chapter 19 of the International Building Code 2009. Comparing to a manually-developed gold standard, 98.7% recall and 87.6% precision in noncompliance detection were achieved.
•A fully-automated approach to code compliance checking in construction is proposed.•Requirements are extracted from the code and formalized into rules, automatically.•Natural language processing & a logic representation enable such full automation.•Semantic transformation aligns design information with regulatory information.•A prototype achieved 98.7% recall & 87.6% precision in noncompliance detection. |
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ISSN: | 0926-5805 1872-7891 |
DOI: | 10.1016/j.autcon.2016.08.027 |