Toward Asphalt Pavement Construction Safety Improvement with Generative Artificial Intelligence

Recent advancements in generative artificial intelligence (GenAI), particularly large language models (LLMs), have shown promise in enhancing safety analysis within the construction industry. This study explores the integration of structured information from the U.S. Pennsylvania Department of Transportation’s job safety analysis (JSA) documents with unstructured accident narratives from the U.S. Occupational Safety and Health Administration’s Integrated Management Information System (IMIS), focusing on asphalt pavement construction—a sector marked by complex operations and hazardous equipment. Multiple LLMs were employed to classify accident narratives across four dimensions: construction type, relevant JSA job and step, environmental or operational influence, and hazard type. This approach aims to identify frequently cited work activities, assess gaps in safety documentation, and improve future hazard recognition. While general job classification achieved moderate success, performance declined for step-level and contextual classifications, largely because of ambiguous language and overlapping job responsibilities in the narratives. Despite these limitations, LLMs uncovered critical patterns. Paver and roller operations emerged as high-risk activities, often influenced by environmental factors such as traffic or weather. Furthermore, exploratory hazard analysis revealed that model-suggested hazard labels were sometimes more contextually appropriate than those in the original IMIS database, indicating opportunities for data refinement. By aligning structured safety documentation with real-world incidents through GenAI, this study highlights a novel pathway for data-driven safety planning in highway construction. While expert oversight remains essential, the results demonstrate the potential of LLMs to support more adaptive, targeted, and proactive approaches to risk assessment and mitigation.