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@article{205317,
        author = {Rushikesh D. Deshmukh},
        title = {AI-Based Automatic Mapping of BIM Elements to Cost Items},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {13},
        number = {1},
        pages = {6077-6082},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=205317},
        abstract = {Building Information Modelling (BIM) has improved quantity extraction and project visualization, but cost estimation in 5D BIM still relies heavily on manual mapping between BIM elements and cost database items. This study presents an AI-based semantic similarity framework that automatically maps BIM elements to corresponding DSR (Data Schedule of Rates) cost items. Using Natural Language Processing (NLP) and sentence embedding techniques, the system understands contextual similarities between BIM descriptions and DSR item descriptions, overcoming the limitations of traditional keyword-based approaches. A Python-based prototype was developed to process BIM data, perform semantic matching, and generate cost estimates automatically. The proposed framework reduces manual effort, improves consistency, and supports the advancement of intelligent 5D BIM cost estimation workflows.},
        keywords = {Building Information Modelling (BIM), 5D BIM, Artificial Intelligence, Natural Language Processing, Semantic Similarity, Cost Estimation, DSR, Construction Automation.},
        month = {June},
        }