This paper describes an approach to extracting a predicate-argument structure (PAS) in building design rule sentences using natural language processing (NLP) and deep learning models. For the computer to reason about the compliance of building design, design rules represented by natural language must be converted into a computer-readable format. The rule interpretation and translation processes are challenging tasks because of the vagueness and ambiguity of natural language. Many studies have proposed approaches to address this problem, but most of these are dependent on manual tasks, which is the bottleneck to expanding the scope of design rule checking to design requirements from various documents. In this paper, we apply deep learning-based NLP techniques for translating design rule sentences into a computer-readable data structure. To apply deep learning-based NLP techniques to the rule interpretation process, we identified the semantic role elements of building design requirements and defined a PAS for design rule checking. Using a bidirectional long short-term memory model with a conditional random field layer, the computer can intelligently analyze constituents of building design rule sentences and automatically extract the logical elements. The proposed approach contributes to broadening the scope of building information modeling-enabled rule checking to any natural language-based design requirements.
|Number of pages||14|
|Journal||Journal of Computational Design and Engineering|
|Publication status||Published - 2020 Oct 1|
Bibliographical noteFunding Information:
This research was supported by a grant (20AUDP-B127891-04) from the Architecture &Urban Development Research Program funded by the Ministry of Land, Infrastructure and Transport of the Korean government.
© 2020 The Author(s). Published by Oxford University Press on behalf of the Society for Computational Design and Engineering.
All Science Journal Classification (ASJC) codes
- Computational Mechanics
- Modelling and Simulation
- Engineering (miscellaneous)
- Human-Computer Interaction
- Computer Graphics and Computer-Aided Design
- Computational Mathematics