# Highway Construction Safety Analysis Using Large Language Models
## 1 Introduction  
The highway construction industry, a critical aspect of infrastructure development, poses significant risks to worker safety. Work zone hazards such as high-speed passing traffic, large construction and maintenance equipment, material movement, and extreme environmental conditions make it a particularly dangerous environment [1]. Statistics from the Bureau of Labor Statistics (BLS) and the Occupational Safety and Health Administration (OSHA) reveal that the construction industry worker fatalities in the United States accounted for a staggering 20.5% of all private industry fatalities in 2014 and 21.1% in 2019 [2, 3]. Incidents also account for huge costs in the construction industry [4]. As the deteriorating condition of highways is a pressing concern, with over 44% of highway systems in the United States exhibiting a poor condition, an increase in projects related to maintenance and rehabilitation is expected in the next years [5]. With these alarming figures, there remains a need for comprehensive accident analyses in the field to help mitigate safety hazards in the highway industry. The primary causes contributing to construction-related fatalities, as identified by OSHA, remain prevailing areas of interest: struck-by accidents, falls, caught-in/between incidents, electrical shock, and others [6]. Multiple studies have also identified the topcontributors to accidents in the industry; for example, historical data indicate that 70% of struck-by accidents resulted from being struck by a falling object or equipment, or being run over by heavy equipment or private vehicles [6]. It is important to recognize that work zone characteristics and the environment exert a significant influence on work zone accidents, injuries, and fatalities [7]. Additionally, human factors, such as worker behavior and ergonomics, play a crucial role in accidents in highway construction zones [1,6]. Even with safety improvements, injuries and fatalities in highway construction and maintenance continue to persist at alarming levels, underscoring the urgent need for more comprehensive safety measures [6]. Data-driven decision making is widely recognized as a pivotal approach to informed decision making in safety analyses, as it fulfills the requirement for effective categorization and analysis of safety incidents in diverse industries to understand their causes, attribute accidents to worker behavior, and enhance safety programs [1,8]. Nevertheless, the current methods employed for incident analysis have some limitations. While incident databases offer valuable insights for case studies, few researchers have explored the potential of utilizing OSHA databases to gain a deeper insight into safety incidents and their underlying causes. For example, [PERSON] et al. (2016) addressed this gap by utilizing the OSHA IMIS database along with machine learning techniques, emphasizing the time-consuming and expensive nature of manual analysis [9]. Furthermore, the examination of accident narratives, which are commonly present in accident reports, is an important approach to the analysis of construction safety data due to the wealth of detail that is available per incident. Researchers have employed natural language processing (NLP) techniques, such as text classification and mining, to extract valuable information from accident narratives [10]. Machine learning algorithms, including support vector machines (SVMs), random forests, and logistic regression, have been utilized to classify and predict accident severity levels [10]. Additionally, deep learning approaches have been explored to classify safety incidents, with a particular focus on understudied areas like near-misses [11]. Machine learning approaches coupled with NLP provide a means to tackle the inherent challenges in conventional methods, offering improved efficiency and depth of analysis [9].