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.

翻译：确保施工项目的安全性、质量与按时竣工至关重要，而施工检查是实现这些目标的关键手段。然而，当前主要依赖人工操作的检查方式常导致效率低下与信息管理不足，难以实现全面、详尽的评估，进而引发监管疏漏与潜在安全隐患。为解决此问题，本文提出了一种名为AutoRepo的新型自动化施工检查报告生成框架。该框架利用无人车高效执行施工检查并采集现场信息，同时借助多模态大语言模型自动生成检查报告。该框架在实际施工场地进行了应用与测试，结果表明其能够加速检查流程、显著减少资源投入，并生成符合监管标准的高质量检查报告。本研究充分揭示了多模态大语言模型在革新施工检查实践中的巨大潜力，标志着向更高效、更安全的施工管理模式迈出了重要的一步。