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Abstract

Traditional clash management in Building Information Management (BIM) is predominantly manual, relying on basic tools that often result in inefficiencies and project delays, especially in large-scale projects. While existing methods have laid a solid foundation, they are often overwhelmed by the volume of clashes that need to be managed, which can result in critical oversights. In this study we mainly address these challenges by developing a conceptual framework that integrates machine learning techniques to automate the clash management process within BIM environments.

The development of a conceptual framework based on a thorough literature review and the validation of this framework through a survey of BIM professionals. Traditional clash management processes, the challenges associated with them, and the potential for automation through advanced technologies have been examined in the literature review. The key findings indicate that Machine Learning (ML) techniques have shown promise in automating tasks like clash detection, filtering, categorization, and prioritization. However, a significant gap is still present in order to achieve a fully automated end-to-end process. 

Results of the survey validate the proposed framework. BIM professionals strongly recommend moving toward adopting automated solutions. Which focuses the attention on the importance of automating the clash management process. While significant strides have been made, achieving a fully automated clash management process in BIM requires further development and real-world validation through pilot projects or case studies. 

This research contributes to the ongoing discourse on BIM automation. It offers a structured approach to incorporate advanced technologies into clash management, with the potential to improve project outcomes by reducing the time, cost, and effort involved in managing clashes. 

Course: Building Information Management - MSc - P2657FTC

Date Deposited: 2025-01-20

URI/permalink: https://library.port.ac.uk/dissert/dis14692.html