AutoRepo: A general framework for multi-modal LLM-based automated construction reporting
Hongxu Pu, Xincong Yang, Jing Li, Runhao Guo, Heng Li

TL;DR
AutoRepo introduces a multimodal LLM-based framework that automates construction inspection reporting by integrating unmanned vehicle data collection with AI-generated reports, enhancing efficiency and safety in construction management.
Contribution
The paper presents a novel multimodal LLM framework for automated construction inspection reporting, demonstrating its effectiveness on real-world sites and its potential to improve safety and efficiency.
Findings
Expedited inspection processes with reduced resource use
Generated reports meet regulatory standards
Effective integration of unmanned vehicles and multimodal LLMs
Abstract
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,…
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Taxonomy
TopicsInfrastructure Maintenance and Monitoring · Occupational Health and Safety Research · BIM and Construction Integration
