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To facilitate a better understanding of building codes, the visualization of the embedded structures of the provisions and requirements of the codes is needed. Existing research efforts in building code compliance checking mostly do not purposefully represent building codes in formats that facilitate human understanding and interaction with the codes, such as XML and hypertext (text with links to other text). Visual programming commonly represents building codes more visually as flowcharts. However, flowcharts are static, and the generation of flowcharts is still manual. To address this lack of interactive visual representation of building code requirement structures, this paper proposes an automated building code structure extraction and visualization method for visualizing building code contents in a way that clearly shows the inter-connections between requirements and allows intuitive user interaction. In this method, to extract the chapter-section-subsection hierarchical structure and cross-reference structure, a new extraction method named Building Code Network Generator (BCNG) is proposed to automatically generate an interactive visualization using a directed network. The performance of the proposed BCNG was empirically tested on Chapters 5 and 10 of the International Building Code 2015, with a resulting precision, recall, and F1-score of 99.4%, 96.3%, and 97.8%, respectively. In addition, the extracted hierarchical and cross-reference structures were displayed using an open-source network visualization tool to facilitate human understanding and interactions with the building code requirements in automated compliance checking systems.
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Unsupervised Machine Learning for Augmented Data Analytics of Building Codes
Existing automated code checking methods/tools are unable to automatically analyze and represent all types of requirements (e.g., requirements that are too complex or that require human judgement). Recent efforts in the area of augmented data analytics have proposed the use of templates to facilitate the analysis of text. However, most of these efforts have constructed such templates manually, which is labor-intensive. More importantly, it is difficult for manually-developed templates to capture the linguistic variations in building codes. More research is, thus, needed to automate the generation of templates to support the tagging and extraction of information from building codes. To address this need, this paper proposes an unsupervised machine-learning based method to extract sentence templates that describe syntactic and semantic features and patterns from building codes. The proposed method is composed of four main steps: (1) data preprocessing; (2) identifying the different groups of sentence fragments using clustering; (3) identifying the fixed parts and the slots in the templates based on the syntactic and semantic patterns of the sentence fragment groups; and (4) evaluating the extracted templates. The proposed method was implemented and tested on a corpus of text from the International Building Code. An accuracy of 0.76 was achieved.
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- Award ID(s):
- 1827733
- PAR ID:
- 10110923
- Date Published:
- Journal Name:
- ASCE International Conference on Computing in Civil Engineering 2019
- Page Range / eLocation ID:
- 74 to 81
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1061/9780784482438.010
