# Highway Construction Safety Analysis Using Large Language Models
## 3 Database and Methods
### OSHA SIR Database Acquisition and Description  
For this study, data from the OSHA Severe Injury Reports (SIR) database were used. OSHA requires employers in the US to report all severe work-related injuries from 1 January  
Figure 1: Data processing, visualization, and LLM usage approach.  
2015. This database was selected due to the completeness and heavy concentration of textual information in comparison to other publicly available databases.  
The OSHA SIR database, covering data from 2015 to 2021, has over 70,000 entries, including all the industry codes from the North American Industry Classification System (NAICS). NAICS Code 237310, which refers to Highway, Street, and Bridge Construction was investigated in this study. This code encompasses a range of activities, from conventional paving to airport runway construction and painting of traffic lines. A total of 1032 accidents with severe injuries were reported under code 237310, about 1.5% of the total reported injuries, ranking the highway construction industry among the top 10 percent of contributors to severe injuries relative to all other industries. Figure 2 demonstrates the distribution of incidents across the United States. The legend in this figure shows arbitrary colors and symbols that were utilized to represent distinct states, with the number of incidents per state enclosed in parentheses.  
Overall, the top three states reporting severe injuries are Texas, Florida, and Pennsylvania, with 18.5%, 14.3%, and 9% of contributions, respectively, but the figures may not account for incidents that are exclusively regulated by state OSHA plans in certain regions. These figures are based on incidents falling under federal OSHA jurisdiction only, excluding those under state jurisdiction.  
The database comprises 26 columns, each providing descriptive information about each incident, including accident date, employer details, accident location and coordinates, and counts of hospitalizations, amputations, and more. For code 237310, 90.2% of accidents resulted in hospitalization, while 17.5% of cases involved an amputation. From the perspective of safety training and accident prevention, the columns containing the final narrative, the accident's nature, the part of the body involved, event title, and source present the most relevant data. Aside from the final narrative, which is a complete textual description of the incident, these columns were coded as per the Occupational Injury and Illness Classification Manual (OMICS) manufactured by the BLS.  
The \"NatureTitle\" signifies the nature of the worker's injury or illness, while the \"Part_of_Body_Title\" specifies the injury's location. The \"EventTitle\" offers a more quantifiable accident description compared to the final narrative, with numerous titles falling into classic accident types like \"struck-by\" or \"fall\". The \"SourceTitle\" pinpoints the primary source of the accident, such as a vehicle or specific equipment.  
Table 1 defines the top entries for each of the specified columns, but due to the plethora of information in these fields, deriving general statistics to identify major causes of accidents is challenging. The coding of injuries adheres to the OMICS system, resulting in a level of detail that may be overly fine-grained, as illustrated in Table 1, where columns like the  
Figure 2: Map of OSHA SIR incidents with highway construction NAICS code.  
source of injury have 1407 different categories, with 230 selected for the unique 237,310 industry code. In contrast, the \"Final_Narrative\" provides a text-based description of the accident, which appears to be relatively correlated with other characterizations. The narratives describing accidents can vary from brief single sentences to detailed descriptions, often containing valuable information that cannot be adequately captured by traditional descriptive statistics, highlighting the valuable role of NLP tools and LLMs in enhancing the analysis of these narratives.