Integrating BIM models and AI models for cost estimationTangara, Faith Sharon (Avtor) Cerovšek, Tomo (Mentor) civil engineeringmaster thesisBIMcost estimationquantity take offartificial intelligencelarge language modelsCost estimation remains a critical part of project management, guiding the budgeting and decision making throughout project phases. Traditional methods are limited due to complexity of building elements, required accuracy, and tight schedules. Despite recent advancements in Building Information Modelling (BIM) there is a growing demand for more accurate, efficient, and automated methods of cost estimation. Therefore, the thesis focus is given to understanding BIM-based Quantity Take-Off (QTO) and automation potential. The goal of the research is to demonstrate the potential of AI to enhance accuracy, efficiency, and streamline cost estimation processes that would support especially cost estimators, and also other project stakeholders. This research explores the potential of AI, specifically Large Language Models (LLMs) like ChatGPT, and other technologies in conjunction with BIM models. By integrating LLM, BIM, and AI cost estimation could be improved in terms of quality and efficiency, while manual work could be reduced significantly. To fully harness the potential of AI and BIM the interaction between LLMs and BIM models should be enhanced, as well as advanced analytics of historical data that would serve as learning sets should be put in place to allow for framework's applicability on diverse project types and scales.[F. S. Tangara]20232023-09-20 07:46:00Magistrsko delo/naloga150515UDK: 004.946:69-047.74(043.3)VisID: 159449COBISS_ID: 187626755sl