A Conceptual AI-based Framework for Clash Triage in Building Information Modeling (BIM)

This article presents a conceptual framework for using artificial intelligence for collision triage in a BIM (Building Information Modeling) environment. Modern collision detection tools generate huge numbers of reports, which directly burdens BIM coordinators and makes it difficult for them to effectively manage the interdisciplinary coordination process. Previous approaches have focused mainly on collision detection itself and simple, rule-based prioritization, rarely exploiting the potential of AI (Artificial Intelligence) methods in the area of post-processing of results. The proposed framework describes a modular system in which collision detection results and data from BIM models, schedules (4D), and cost estimates (5D) are processed by a set of AI components. These include: a classifier that filters out irrelevant collisions (noise), algorithms that group recurring collisions into single design problems, a model that assesses the significance of collisions by determining a composite 'AI Triage Score' indicator, and a module that assigns responsibility to the appropriate trades and process participants. The article also discusses a potential way to integrate the framework into the existing BIM workflow and possible scenarios for its validation based on case studies and expert evaluation. The proposed conceptual framework represents a step towards moving from manual, intuitive collision triage to a data- and AI-based approach, which can contribute to increased coordination efficiency, reduced risk of errors, and better use of design resources.