From pixels to rating—A semiautomated system linking multi‐damage segmentation and condition rating in concrete bridge inspections

While machine learning (ML) has advanced image-based damage detection, a critical gap remains: the automated translation of detected damage into standardized condition ratings used in structural assessments. Most existing approaches stop at semantic segmentation, overlooking the damage rating step essential for practical inspections. This paper presents a semiautomated system that bridges this gap by linking multi-label damage segmentation with condition rating prediction. Our contributions are: (1) a data-driven label taxonomy for damage segmentation, derived from statistical and semantic analysis of 2.2 million inspection records, and designed to support downstream condition rating; (2) a pipeline for converting textual inspection records into structured training data for automated condition rating, and a set of custom bidirectional long short-term memory (LSTM) models achieving up to 99 % $99 \,\%$ F1-score on this task; and (3) a reference system architecture integrating image segmentation and text-based damage rating within an interactive 3D inspection interface. The system demonstrates how integrating damage detection and condition rating within an interactive 3D interface can streamline inspection documentation and enhance decision support for concrete structures. Developed in compliance with German inspection standards and designed for adaptability, the system architecture offers a transferable framework for embedding ML-based automation into digital inspection workflows, ensuring that all components, from damage detection to condition rating, are aligned in an end-to-end process.