A deep-learning method for evaluating semantically-rich building code annotations

Ruichuan Zhang, Nora El-Gohary

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Existing automated code checking (ACC) methods require the extraction of requirements from building codes and the representation of these requirements in a computer-processable form. Although these methods have achieved different levels of performance, all of them are still unable to represent all types of building-code requirements. There is, thus, a need to enhance the semantic representations of building codes towards facilitating the representation of all requirements. To address this need, this paper first proposes a new approach to annotate and represent building-code sentences using requirement units that consist of semantic information elements and simple logic operators. To evaluate the proposed building-code annotation approach, this paper also proposes a new natural language generation (NLG)-based method for evaluating annotation quality. The proposed method consists of four steps: data preparation, data preprocessing, NLG model development and training, and sentence evaluation. Sentences from the International Code Council (ICC) building codes were used in the evaluation.

Original languageEnglish (US)
Title of host publicationEG-ICE 2020 Workshop on Intelligent Computing in Engineering, Proceedings
EditorsLucian-Constantin Ungureanu, Timo Hartmann
PublisherUniversitatsverlag der TU Berlin
Pages285-293
Number of pages9
ISBN (Electronic)9783798331556
StatePublished - 2020
Event27th EG-ICE International Workshop on Intelligent Computing in Engineering 2020 - Virtual, Online, Germany
Duration: Jul 1 2020Jul 4 2020

Publication series

NameEG-ICE 2020 Workshop on Intelligent Computing in Engineering, Proceedings

Conference

Conference27th EG-ICE International Workshop on Intelligent Computing in Engineering 2020
CountryGermany
CityVirtual, Online
Period7/1/207/4/20

ASJC Scopus subject areas

  • Computer Science Applications
  • Engineering(all)

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