Knowledge-infused framework for crack diagnosis, condition assessment, and maintenance planning of concrete structures

Accurate diagnosis of cracks in concrete structures requires integrating data-driven perception with mechanism-based knowledge. This paper presents a knowledge-infused framework integrating crack perception, knowledge-guided reasoning, and human-in-the-loop validation, implemented as an engineer-friendly interactive system for interpretable assessment. A domain-specific knowledge graph containing 530 nodes and 716 edges is constructed to represent crack characteristics, deterioration mechanisms, and maintenance strategies, and Retrieval-Augmented Generation (RAG) is integrated to enhance standards-guided reasoning and improve code compliance. The crack detection and quantification module achieved high accuracy (R 2 = 0.976, MAE = 0.505 mm) with an average processing time of 4.6 s per image. The framework is completed within 164.6 s per case. Under the representative bridge inspection scenario considered in this study, the proposed framework is estimated to reduce inspection labor cost and time by 78.3% and 68.7%, respectively, compared with manual inspection. The framework provides an efficient, transparent, and standards-guided solution for intelligent infrastructure maintenance.