Predicting Ability of Dynamic Balance in Construction Workers Based on Demographic Information and Anthropometric Dimensions

Abdolahi, F; Variani, Ali Safari; Varmazyar, Sakineh

doi:10.1016/j.shaw.2021.07.009

Cited by 1 publication

(2 citation statements)

References 41 publications

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“…While proactive measures can address some risks, other challenging factors like overall negligence, inadequate site management, and insufficient training may require more intervention [17]. Common incidents, including man/machine interface and side falls of materials, especially for workers at heights, contribute to the hazardous nature, with falling hazards representing a significant portion of fatalities [3,15]. Recognizing these factors is crucial, as unidentified hazards can lead to safety incidents and work-related injuries, emphasizing the need for new methods to improve intervention [18].…”

Section: Status Of Construction Safetymentioning

confidence: 99%

“…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].…”

Section: Introductionmentioning

confidence: 99%

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Highway Construction Safety Analysis Using Large Language Models

Smetana,

Salles de Salles,

Sukharev

et al. 2024

Applied Sciences

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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.

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