General-purpose vision-language models demonstrate strong performance in everyday domains but struggle with specialized technical fields requiring precise terminology, structured reasoning, and adherence to engineering standards. This work addresses whether domain-specific instruction tuning can enable comprehensive pavement condition assessment through vision-language models. PaveInstruct, a dataset containing 278,889 image-instruction-response pairs spanning 32 task types, was created by unifying annotations from nine heterogeneous pavement datasets. PaveGPT, a pavement foundation model trained on this dataset, was evaluated against state-of-the-art vision-language models across perception, understanding, and reasoning tasks. Instruction tuning transformed model capabilities, achieving improvements exceeding 20% in spatial grounding, reasoning, and generation tasks while producing ASTM D6433-compliant outputs. These results enable transportation agencies to deploy unified conversational assessment tools that replace multiple specialized systems, simplifying workflows and reducing technical expertise requirements. The approach establishes a pathway for developing instruction-driven AI systems across infrastructure domains including bridge inspection, railway maintenance, and building condition assessment.

翻译：通用视觉语言模型在日常领域展现出强劲性能，但在需要精确术语、结构化推理及符合工程标准的专业技术领域存在局限性。本研究旨在探究领域特定指令微调能否通过视觉语言模型实现路面状况的全面评估。通过整合九个异构路面数据集的标注信息，我们构建了包含278,889条图像-指令-响应对的PaveInstruct数据集，覆盖32种任务类型。基于该数据集训练的PaveGPT路面基础模型，在感知、理解与推理任务中与现有最优视觉语言模型进行了对比评估。指令微调显著提升了模型能力，在空间定位、推理与生成任务中实现超过20%的性能提升，同时生成符合ASTM D6433标准的输出结果。这些成果使交通管理部门能够部署统一的对话式评估工具，替代多个专用系统，从而简化工作流程并降低专业技术门槛。本研究建立的范式可推广至桥梁检测、铁路养护及建筑状况评估等基础设施领域的指令驱动型AI系统开发。