Transformer language model for mapping construction schedule activities to uniformat categories

Research output: Contribution to journalArticlepeer-review


Creating consistency among project schedule data, BIM, and payment applications requires activities in a construction schedule to be mapped with the most relevant ASTM Uniformat classifications. To do so, we introduce UNIFORMATBRIDGE, a new transformer-based natural language processing model, that automatically labels activities in a project schedule with Uniformat classification. Our model introduces construction sequencing tokens that capture logistically-constrained predecessor and successor activities into BERT architecture. We also introduce a dataset of real-world construction project schedules with their ground-truth Uniformat classifications for validation. Experimental results using this dataset achieve F1-scores of 0.93 and 0.87 when matching unstructured schedule data to Uniformat Level 2 and 3 classifications, respectively. We share how our method unlocks development of new techniques to (1) automatically create 4D BIM, and (2) computer-vision progress monitoring to tie semantic segmentation of reality capture data based on Uniformat classes against schedule or payment application data structures, with/without BIM.

Original languageEnglish (US)
Article number105183
JournalAutomation in Construction
StatePublished - Jan 2024


  • Artificial intelligence
  • Building Information Modeling
  • Construction planning
  • Machine learning
  • Natural language processing
  • Project controls

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Civil and Structural Engineering
  • Building and Construction


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