期刊
AUTOMATION IN CONSTRUCTION
卷 141, 期 -, 页码 -出版社
ELSEVIER
DOI: 10.1016/j.autcon.2022.104403
关键词
Building information modeling; Transfer learning; Natural language understanding; Robustly optimized BERT pretraining approach; Text classification; Machine learning; Virtual assistant
This research developed a transfer learning-based text classification method using the RoBERTa neural network to accurately classify different building information-related queries into predefined categories, providing information retrieval support for a virtual assistant.
Retrieving queried information from building information models (BIM) requires experience in structured query languages and manipulation of BIM software. Artificial Intelligence (AI)-based spoken dialogue systems provide more opportunities for information retrieval from building information models via natural language queries. This research developed a transfer learning-based text classification (TC) method to classify different queries into predefined categories for an intelligent building information spoken dialogue system (iBISDS), a virtual assistant that provides information retrieval support for construction project team members. The architecture of the TC neural network (NN) was built based on the pre-trained Robustly Optimized BERT Pretraining Approach (RoBERTa). After the training process, the re-trained and fine-tuned RoBERTa NN achieved a precision of 99.76%, a recall of 99.76%, and an F1 score of 99.76% on the testing dataset. The experimental results indicated that the developed NN algorithm for TC can relatively accurately classify different building information-related queries into pre-defined TC categories.
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