  Ensuring the safety, quality, and timely completion of construction projects
is paramount, with construction inspections serving as a vital instrument
towards these goals. Nevertheless, the predominantly manual approach of
present-day inspections frequently results in inefficiencies and inadequate
information management. Such methods often fall short of providing holistic,
exhaustive assessments, consequently engendering regulatory oversights and
potential safety hazards. To address this issue, this paper presents a novel
framework named AutoRepo for automated generation of construction inspection
reports. The unmanned vehicles efficiently perform construction inspections and
collect scene information, while the multimodal large language models (LLMs)
are leveraged to automatically generate the inspection reports. The framework
was applied and tested on a real-world construction site, demonstrating its
potential to expedite the inspection process, significantly reduce resource
allocation, and produce high-quality, regulatory standard-compliant inspection
reports. This research thus underscores the immense potential of multimodal
large language models in revolutionizing construction inspection practices,
signaling a significant leap forward towards a more efficient and safer
construction management paradigm.
