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Construction site hazards and safety concerns have been a longstanding issue within the industry. These concerns extend beyond immediate physical risks to workers and public safety. Ensuring safety in construction is pivotal, as it not only safeguards lives but also has a significant societal impact. Previous studies have explored various methods and strategies to enhance construction site safety. However, there remains a gap in understanding the intricate interplay between contributing factors, personal attributes, and accident occurrence. This research aims to fill this gap by employing advanced data analysis techniques, including text mining, time series, comparative, correlation analyses. In this regard, we present a novel approach to address construction site safety. We begin by manually collecting data from the National Institute for Occupational Safety and Health (NIOSH) accident reports and subsequently employ an automatic algorithm for data cleaning and extraction. The data we collect includes information on Gender, Age, Contributing factors, Cause of Death, Recommendations by experts, State, and Summary from these reports. Through these advanced data analysis techniques, we aim to identify the most effective strategies for reducing the occurrence of accidents in the construction industry, thereby improving overall safety performance.
Piri et al. (Sun,) studied this question.