A Benchmark Image Dataset of Reinforced Concrete Beam–Column Joint Failures

Reinforced concrete beam–column joints are critical structural components whose damage patterns provide important evidence for diagnosing structural failure mechanisms. However, publicly available visual datasets dedicated to beam–column joint failures remain highly limited, especially those with expert-verified structural annotations. We introduce a curated beam–column joint image dataset with expert-verified annotations for structural damage analysis. Each sample is associated with categorical metadata describing joint configuration, failure mechanism, damage type, and graded damage severity, together with expert-written textual diagnostic descriptions. The dataset contains 572 beam–column joint images and 1,716 image–text diagnostic description pairs. To demonstrate the technical usability of the dataset, we provide baseline experiments for several representative tasks, including failure-mode classification, image-to-text diagnostic description generation, and text-conditioned image generation. Experimental results show that the dataset supports reproducible benchmarking across both vision and vision–language modeling tasks. Overall, the dataset provides a standardized benchmark for structural damage recognition, multimodal representation learning, and generative modeling in structural engineering.