  Interpreting regulatory documents or building codes into computer-processable
formats is essential for the intelligent design and construction of buildings
and infrastructures. Although automated rule interpretation (ARI) methods have
been investigated for years, most of them highly depend on the early and manual
filtering of interpretable clauses from a building code. While few of them
considered machine interpretability, which represents the potential to be
transformed into a computer-processable format, from both clause- and
document-level. Therefore, this research aims to propose a novel approach to
automatically evaluate and enhance the machine interpretability of single
clause and building codes. First, a few categories are introduced to classify
each clause in a building code considering the requirements for rule
interpretation, and a dataset is developed for model training. Then, an
efficient text classification model is developed based on a pretrained
domain-specific language model and transfer learning techniques. Finally, a
quantitative evaluation method is proposed to assess the overall
interpretability of building codes. Experiments show that the proposed text
classification algorithm outperforms the existing CNN- or RNN-based methods,
improving the F1-score from 72.16% to 93.60%. It is also illustrated that the
proposed classification method can enhance downstream ARI methods with an
improvement of 4%. Furthermore, analyzing the results of more than 150 building
codes in China showed that their average interpretability is 34.40%, which
implies that it is still hard to fully transform the entire regulatory document
into computer-processable formats. It is also argued that the interpretability
of building codes should be further improved both from the human side and the
machine side.
