Construction of an Intelligent Risk Identification System for Highway Flood Damage Based on Multimodal Large Models

Under the increasing threat of extreme weather events, road infrastructure faces significant risks of flood-induced damage. Traditional manual inspection methods are insufficient for modern highway emergency response, which requires higher efficiency and accuracy. To enhance the precision and accuracy of flood damage identification, this study proposes an intelligent recognition system that integrates a multimodal large language model with a structured knowledge base. The system constructs a professional repository covering eight typical categories of flood damage, including roadbed, pavement, and bridge components, with associated attributes, visual features, and mitigation strategies. A vectorized indexing mechanism enables fine-grained semantic retrieval, while task-specific templates and prompt engineering guide the multimodal model, such as Qwen-VL-Max, which extracts risk elements from image–text inputs and generating structured identification results with expert recommendations. The system is evaluated on a real-world highway flood damage dataset. The results show that the knowledge-enhanced model performs better than the baseline and prompt-optimized models. It reaches 91.5% average accuracy, a semantic relevance score of 4.58 out of 5, and 85% robustness under difficult conditions. These results highlight the strong domain adaptability and practical value for real-time flood damage assessment and emergency response.