AI-Driven Component Prioritization for Yingxian Pagoda Conservation in HBIM

Abstract. Based on the difficulties in addressing the current state of the structural instability of the Yingxian Wooden Pagoda, a hypothesis is established in this research to combine the Heritage Building Information Modelling(HBIM) technology with the text comprehension ability of Artificial Intelligence(AI) for an effective conservation strategy. The key method is to train an AI study model with Natural Language Processing (NLP) that could be plugged into the BIM software and be able to understand the current damage report of this pagoda from the aspect of structural risk. The problematic architectural components would be classified and categorized in labels with highlighted visual effects in the software. This research could integrate the existing AI diagnosis technique with BIM software to enhance efficiency and flexibility in architectural conservation. It enhances structural analysis precision with automated assessments applied to support a data-driven conservation framework.