AutoRepo: A general framework for multi-modal LLM-based automated construction reporting
This addresses the problem of inefficient and unsafe construction inspections for industry stakeholders, representing an incremental application of existing technologies to a new domain.
The paper tackles inefficiencies in manual construction inspections by introducing AutoRepo, a framework that uses unmanned vehicles and multimodal LLMs to automatically generate inspection reports, tested on a real-world site to expedite the process and reduce resource allocation.
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.