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

[Display omitted] • An NLP transformer for relationship prediction between look-ahead tasks and master schedule activities with 82.1% Precision. • A generative model for creating look-ahead planning task descriptions based on a master schedule activity description. • A semi-automated human-in-the-loop method for linking look-ahead tasks to master schedule activities with 76.5% Precision. • A formalized representation for matching look-ahead planning tasks to master schedule activities. 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 1 Access to the code and a sample of the dataset can be granted upon direct request from the authors. 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. [ABSTRACT FROM AUTHOR]