Zhang, Zijing;
Ma, Ling;
(2023)
Using machine learning for automated detection of ambiguity in building requirements.
In:
Proceedings of the 2023 European Conference on Computing in Construction and the 40th International CIB W78 Conference.
European Council on Computing in Construction: Heraklion, Greece.
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Abstract
The rule interpretation step is yet to be fully automated in the compliance checking process, hindering the automation of compliance checking. Whilst existing research has developed numerous methods for automated interpretation of building requirements, none can identify ambiguous requirements. As part of interpreting ambiguous clauses automatically, this research proposed a supervised machine learning method to detect ambiguity automatically, where the best-performing model achieved recall, precision and accuracy scores of 99.0%, 71.1%, and 78.2%, respectively. This research contributes to the body of knowledge by developing a method for automated detection of ambiguity in building requirements to support automated compliance checking.
| Type: | Proceedings paper |
|---|---|
| Title: | Using machine learning for automated detection of ambiguity in building requirements |
| Event: | 2023 European Conference on Computing in Construction and the 40th International CIB W78 Conference |
| Location: | Heraklion, Crete, Greece |
| ISBN-13: | 978-0-701702-73-1 |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.35490/EC3.2023.211 |
| Publisher version: | https://ec-3.org/publications/conference/paper/?id... |
| Language: | English |
| Additional information: | This version is the version of record. For information on re-use, please refer to the publisher’s terms and conditions. |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of the Built Environment |
| URI: | https://discovery-pp.ucl.ac.uk/id/eprint/10174754 |
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