A machine learning-based method for building code requirement hierarchy extraction

R. Zhang, N. El-Gohary

Research output: Contribution to conferencePaperpeer-review

Abstract

Most of the existing automated code compliance checking (ACC) methods are unable to fully automatically convert complex building-code requirements into computer-processable forms. Such complex requirements usually have hierarchically complex clause and sentence structures. There is, thus, a need to decompose such complex requirements into hierarchies of much smaller, manageable requirement units that would be processable using most of the existing ACC methods. Rule-based methods have been used to deal with such complex requirements and have achieved high performance. However, they lack scalability, because the rules are developed manually and need to be updated and/or adapted when applied to a different type of building code. More research is, thus, needed to develop a scalable method to automatically convert the complex requirements into hierarchies of requirement units to facilitate the succeeding steps of ACC such as information extraction and compliance reasoning. To address this need, this paper proposes a new, machine learning-based method to automatically extract requirement hierarchies from building codes. The proposed method consists of five main steps: (1) data preparation and preprocessing; (2) data adaptation; (3) deep neural network model training for dependency parsing; (4) automated requirement segmentation and restriction interpretation based on the extracted dependencies; and (5) evaluation. The proposed method was trained using the English Treebank data; and was tested on sentences from the 2009 International Building Code (IBC) and the Champaign 2015 IBC Amendments. The preliminary results show that the proposed method achieved an average normalized edit distance of 0.32, a precision of 89%, a recall of 76%, and an F1-measure of 82%, which indicates good requirement hierarchy extraction performance.

Original languageEnglish (US)
StatePublished - 2019
Event2019 Canadian Society for Civil Engineering Annual Conference, CSCE 2019 - Laval, Canada
Duration: Jun 12 2019Jun 15 2019

Conference

Conference2019 Canadian Society for Civil Engineering Annual Conference, CSCE 2019
CountryCanada
CityLaval
Period6/12/196/15/19

ASJC Scopus subject areas

  • Engineering(all)

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