Transformer machine learning language model for auto-alignment of long-term and short-term plans in construction

Fouad Amer, Yoonhwa Jung, Mani Golparvar-Fard

Research output: Contribution to journalArticlepeer-review


In construction, master schedules and look-ahead plans are created at different times (monthly vs. weekly), by different personas (planner vs. superintendent), with different software (scheduling solution vs. spreadsheet), and at different levels of granularity (milestones vs. production details). Their full-alignment is essential for project coordination, progress updating, and payment application reviews, and its absence may lead to costly litigation. This paper presents the first attempt to automate linking look-ahead planning tasks to master-schedule activities following an NLP-based multi-stage ranking formulation. Our model employs distance-based matching for candidate generation and a Transformer architecture for final matching.1 Validation results from real-world projects demonstrate that the method helps planners match look-ahead planning tasks to master schedule activities by presenting a list of top-five matches with a precision of 76.5%. We also show that the method helps superintendents create look-ahead plans from a master schedule by generating lists of tasks based on activity descriptions.

Original languageEnglish (US)
Article number103929
JournalAutomation in Construction
StatePublished - Dec 2021


  • Artificial intelligence
  • 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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