Extracting Structured Requirements from Unstructured Building Technical Specifications for Building Information Modeling

• URL: http://arxiv.org/abs/2508.13833v1
• Date: Tue, 19 Aug 2025 13:55:41 GMT
• Title: Extracting Structured Requirements from Unstructured Building Technical Specifications for Building Information Modeling
• Authors: Insaf Nahri, Romain Pinquié, Philippe Véron, Nicolas Bus, Mathieu Thorel,
• Abstract summary: This study explores the integration of Building Information Modeling with Natural Language Processing (NLP)<n>It aims to automate the extraction of requirements from unstructured French Building Technical Specification documents within the construction industry.<n>The results indicate that CamemBERT and Fr_core_news_lg exhibited superior performance in NER, achieving F1-scores over 90%, while Random Forest proved most effective in RE, with an F1 score above 80%.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: This study explores the integration of Building Information Modeling (BIM) with Natural Language Processing (NLP) to automate the extraction of requirements from unstructured French Building Technical Specification (BTS) documents within the construction industry. Employing Named Entity Recognition (NER) and Relation Extraction (RE) techniques, the study leverages the transformer-based model CamemBERT and applies transfer learning with the French language model Fr\_core\_news\_lg, both pre-trained on a large French corpus in the general domain. To benchmark these models, additional approaches ranging from rule-based to deep learning-based methods are developed. For RE, four different supervised models, including Random Forest, are implemented using a custom feature vector. A hand-crafted annotated dataset is used to compare the effectiveness of NER approaches and RE models. Results indicate that CamemBERT and Fr\_core\_news\_lg exhibited superior performance in NER, achieving F1-scores over 90\%, while Random Forest proved most effective in RE, with an F1 score above 80\%. The outcomes are intended to be represented as a knowledge graph in future work to further enhance automatic verification systems.

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