The highway construction industry carries substantial safety risks for workers, necessitating thorough accident analyses to implement effective preventive measures. Current research lacks comprehensive investigations into safety incidents, relying heavily on conventional statistical methods and overlooking valuable textual information in publicly available databases. This study leverages a state-of-the-art large language model (LLM), specifically OpenAI’s GPT-3.5 model. The primary focus is to enhance text-based incident analysis that is sourced from OSHA’s Severe Injury Reports (SIR) database. By incorporating novel natural language processing (NLP) techniques, dimensionality reduction, clustering algorithms, and LLM prompting of incident narratives, the study aims to develop an approach to the analysis of major accident causes in highway construction. The resulting cluster analysis, coupled with LLM summarization and cause identification, reveals the major accident types, such as heat-related and struck-by injuries, as well as commonalities between incidents. This research showcases the potential of artificial intelligence (AI) and LLM technology in data-driven analysis. By efficiently processing textual data and providing insightful analysis, the study fosters practical implications for safety professionals and the development of more effective accident prevention and intervention strategies within the industry.

State Departments of Transportation (DOTs) are facing workforce shortages and a decline in experienced construction workers. One approach to address this problem is through the active adoption of modern technologies, particularly artificial intelligence (AI) tools. Multimodal large language models (LLMs) offer emerging capabilities, such as instruction following and question answering, making them suitable for numerous practical applications in civil engineering. A key area of interest is effectively communicating complex concepts to train engineers and construction workers; however, there remains a need for a structured approach to selecting appropriate models and evaluating LLMs’ knowledge for specialized engineering areas. This paper presents an evaluation of twenty-six different LLMs using over 100 automatically generated questions, revealing a wide range of accuracies in concrete pavement construction expertise. The proposed methodology uses carefully selected, domain-specific documents and best practices to automatically build a customized framework for assessing model performance in niche fields. It also demonstrates that techniques like the “Retry” method and the integration of domain-specific information through retrieval augmented generation (RAG) can significantly enhance LLM accuracy. The results indicate that open-source, small- and medium-sized models combined with RAG, which can run efficiently on laptop computers, could be deployed for future applications. These techniques show promise for model improvement without additional fine-tuning or retraining, significantly reducing the computational requirements for tailored engineering tasks.