A Text Classification-Based Approach for Evaluating and Enhancing the Machine Interpretability of Building Codes

TL;DR

This paper introduces a novel text classification approach using domain-specific language models to evaluate and improve the machine interpretability of building codes, aiming to facilitate automated rule interpretation.

Contribution

It presents a new classification method and a quantitative evaluation framework to assess and enhance the machine interpretability of building codes at clause and document levels.

Findings

The proposed model achieves an F1-score of 93.60%, outperforming existing methods.

The classification method improves downstream automated rule interpretation by 4%.

Average interpretability of Chinese building codes is 34.40%, indicating room for improvement.

Abstract

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.