Abstract

This paper explores the integration of a novel Ontology for Concrete Surface Defects (OCSD) with a Large Language Model (LLM), specifically GPT-4o (omni), to enhance defect diagnosis and repair strategies in concrete structures. While LLMs independently offer significant reasoning and natural language capabilities, this study demonstrates the value of combining their interpretative power with the structured knowledge representation provided by OCSD. By enabling adaptive reasoning, where the LLM relies on the ontology's domain-specific relationships and thresholds, and updates its conclusions for specific defect types, the system flexibly adjusts its decision-making based on context. This integration improves diagnosis accuracy, reasoning transparency, and decision-making efficiency. The proposed method is validated through a case study, highlighting the synergy of OCSD and GPT-4o in addressing challenges in defects diagnosis and repair.