Project requirements prioritization through NLP-driven classification and adjusted work items analysis

Project requirements indicate specific works, events, or conditions that should be fulfilled to ensure the construction project success within planned budgets and times. To effectively manage project requirements, requirement prioritization allows for the proper allocation of limited project resources by determining the relative importance and urgency of different requirements. However, because project requirements are typically communicated through textual data in documents, the current approach to prioritizing requirements heavily relies on individuals' expertise, practical knowledge, and experiences. This subjective judgment-based process poses a challenge in ensuring consistent and reliable prioritization, because there may be variations in practitioners' prioritization results. Moreover, a large amount of text in documents can complicate capturing significant requirements within limited bidding times. To address these issues, this study proposes a novel method using historical data analysis and computational techniques. This study adopts historical change orders in order to evaluate impact levels of adjusted work items during construction and natural language processing (NLP) techniques, which enable the automated classification of requirements by the most-related work items. This study conducts a case study by examining documents from resurfacing projects and validating the feasibility and effectiveness of the proposed method. It will also provide a cornerstone for a smarter review and understanding of project documentation and improved decision-making for project planning.