Leveraging Large Language Models (LLM) and Machine Learning for Extracting Conditions that Impact Safety Rating of Building Façades

Defective building façades pose significant safety risks and require regular inspection to prevent accidents and ensure public safety. One challenge faced is the lack of guidelines to help inspectors rate the condition of façades given the identified defects. Current means fail to help inspectors determine how defect types, materials, and façade components relate to safety ratings. This study utilizes large language models (LLMs) and machine learning methods to analyze historical façade inspection reports to extract relationships between defect types and their characteristics, façade materials, and façade components to assess their impact on assigned safety ratings, serving as the basis for such a needed guideline for consistent risk rating. This approach also reveals patterns in conditions and components that are most prone to unsafe states. We also demonstrated that building components play the most significant role in determining safety risks, surpassing defect and material types. Additionally, cracks in terra cotta ornamentation were uniquely linked to unsafe states due to their high fall risk. These findings help to uncover the reasoning behind safety ratings, contributing to the development of standardized rating guidelines to reduce discrepancies and ensure consistent risk assessments across inspections.