A Text Classification-based Approach for Evaluating and Enhancing the Machine Interpretability of Building Codes
Published in Engineering Applications of Artificial Intelligence, 2023
This study proposes a text classification approach based on a pretrained large language model, to identify computer-processible clauses and to evaluate the machine interpretability of building codes. Result show that the proposed algorithm outperforms SOTA methods with an F1-score of 93.6%, and could further improve downstream rule interpretation algorithm by 4%. It is also revealed that the overall interpretability of building codes is only 34.4%.
Recommended citation: Zheng, Z., Zhou, Y.C., Chen, K.Y., Lu, X.Z., She, Z.T., Lin, J.R.* (2024). A Text Classification-based Approach for Evaluating and Enhancing the Machine Interpretability of Building Codes. Engineering Applications of Artificial Intelligence, 127PA, 107207. doi: 10.1016/j.engappai.2023.107207 http://doi.org/10.1016/j.engappai.2023.107207