Meet2Mitigate: An LLM-powered framework for real-time issue identification and mitigation from construction meeting discourse

Construction meetings are essential for bringing together project participants to coordinate efforts, identify problems, and make decisions. Previous studies on meeting analysis relied on manual approaches to identify isolated pieces of information but struggled with providing a high-level overview that targeted real-time problem identification and resolution. Despite the rich discussions that occur, the sheer volume of information exchanged can make it difficult to discern key issues, decisions, and action items. Recent advancements in large language models (LLMs) provide sophisticated natural language processing capabilities that can effectively distill essential information and highlight actionable insights from meeting transcripts. However, these technologies are often underutilized in practice, despite their potential to significantly enhance the analysis and management of meeting data. This study introduced the Meet2Mitigate (M2M) framework, which integrates cutting-edge technologies, including speaker diarization, automatic speech recognition (ASR), LLMs, and retrieval-augmented generation (RAG) to revolutionize how construction meetings are captured and analyzed. In this framework, construction meeting recordings can be converted into a structured format, differentiated by timestamps, speakers, and corresponding contents. Different speakers’ dialogues are then summarized to extract the main project-related issues. For quick mitigation responses, this framework combines LLMs with a retrieval mechanism to access the Construction Industry Institute (CII) Best Practices (BPs) knowledge pool, generating detailed action items to drive problem-solving. The validation results demonstrated that the M2M prototype can automatically generate a tailored end-to-end problem-to-solution report in real time by only using a meeting recording file.