TY - GEN
T1 - AI-POWERED APPLICATION FOR CONSTRUCTION SCHEDULE MANAGEMENT USING NATURAL LANGUAGE
AU - Pal, Aritra
AU - Murguia, Danny
AU - Middleton, Campbell
N1 - Publisher Copyright:
© 2025, European Council on Computing in Construction (EC3). All rights reserved.
PY - 2025
Y1 - 2025
N2 - Project schedules are critical for construction management, yet they remain inaccessible to many professionals due to complex scheduling software and costly licenses. As a result, site engineers and last planners rely on static, outdated schedule copies, limiting proactive decision-making. This study proposes an AI-powered schedule management application that enables natural language interaction with project schedules. By leveraging knowledge graphs (KG) and natural language processing (NLP), the app enhances accessibility, reduces inefficiencies, and ensures real-time interaction with updated schedules. Central to this approach is the improvement of Human-Data Interaction (HDI), allowing users without technical scheduling expertise to intuitively engage with complex project data. The results demonstrate improved usability and engagement with schedules. This research contributes an AI-powered schedule management solution to bridge the gap between scheduling expertise and project execution.
AB - Project schedules are critical for construction management, yet they remain inaccessible to many professionals due to complex scheduling software and costly licenses. As a result, site engineers and last planners rely on static, outdated schedule copies, limiting proactive decision-making. This study proposes an AI-powered schedule management application that enables natural language interaction with project schedules. By leveraging knowledge graphs (KG) and natural language processing (NLP), the app enhances accessibility, reduces inefficiencies, and ensures real-time interaction with updated schedules. Central to this approach is the improvement of Human-Data Interaction (HDI), allowing users without technical scheduling expertise to intuitively engage with complex project data. The results demonstrate improved usability and engagement with schedules. This research contributes an AI-powered schedule management solution to bridge the gap between scheduling expertise and project execution.
KW - Knowledge Graph
KW - Large Language Models
KW - Natural Language Processing
KW - Schedule Management
UR - https://www.scopus.com/pages/publications/105029486492
U2 - 10.35490/EC3.2025.407
DO - 10.35490/EC3.2025.407
M3 - Conference contribution
AN - SCOPUS:105029486492
SN - 9789083451312
T3 - Proceedings of the European Conference on Computing in Construction
BT - Proceedings of the 2025 European Conference on Computing in Construction and 42nd International CIB W78 Conference on Information Technology in Construction, 2025
A2 - Petrova, Ekaterina
A2 - Srećković, Marijana
A2 - Meda, Pedro
A2 - Soman, Ranjith K.
A2 - Hall, Daniel
A2 - Beetz, Jakob
A2 - McArthur, Jenn
PB - European Council on Computing in Construction (EC3)
T2 - European Conference on Computing in Construction, EC3 2025 and 42nd International CIB W78 Conference on IT in Construction, 2025
Y2 - 14 July 2025 through 17 July 2025
ER -