A RAG-enhanced multimodal large language model framework for culvert inspection

Despite their importance for maintaining infrastructure integrity, manual culvert inspections pose significant operational challenges for agencies such as the Utah Department of Transportation (UDOT) due to high labor demands, subjective assessments, and variability in evaluation outcomes. This study investigates the application of computer vision for automated culvert inspection, leveraging both supervised machine learning and emerging vision–language models. We proposed a retrieval-augmented generation (RAG)–enhanced multimodal large language model (MLLM) framework that interprets culvert-condition imagery based on the culvert inspection manual. By embedding a small set of curated visual exemplars into a shared space, we enabled image-based retrieval to ground model reasoning alongside the full inspection manual. For the MLLM component, we selected three open-source models (Qwen3-VL, Gemma-3, LLaVA-OneVision) and one proprietary model (Gemini-3-Flash). We evaluated performance using precision, recall, and F1-score, alongside a practical “most-severe-label” decision rule to simulate maintenance prioritization. Results showed that the RAG-enhanced Gemini-3-Flash achieved the highest severity accuracy (91.16%), outperforming the supervised ConvNeXt multi-label baseline (84.54%) and other evaluated MLLMs. Overall, the findings suggest that RAG-enhanced Gemini-3-Flash can improve the reliability of severity-relevant decisions while reducing data-labeling requirements by approximately 99% (from thousands of training images to just 127 exemplars) and the GPU-intensive training burden, with the primary trade-offs being reliance on proprietary APIs and token-based inference costs.