Akademik Veri Yönetim Sistemi
7. international congress on contemporary scientific research, Rome, İtalya, 30 Mart - 06 Nisan 2025, ss.1-7, (Tam Metin Bildiri)
Occupational incidents, which may result in death or cause lasting harm to an individual's
physical or mental integrity, occur due to unforeseen events at the workplace and are typically
linked to the inherent hazards and negligence commonly present in the work. This study focuses
on occupational accidents in the high-risk construction industry and emphasizes the
significance of proactively identifying potential risks and determining appropriate interventions
A dataset of 65,000 work accidents in Turkey was analysed using the Random Forest Machine
Learning technique, calculating risk factors related to personal characteristics, the work
environment, machinery utilized, injured body regions, and injury types. The obtained risk
coefficients were integrated into the developed Occupational Health and Safety (OHS) Risk
Analysis program, which provided comprehensive risk analysis covering both personal and
workplace aspects. This program delivers comprehensive quantitative and qualitative outcomes
regarding which part of the body and how an accident might occur, using scenarios derived
from a thorough analysis of all hazardous machines in the workplace to identify potential
hazards in the working environment when proper precautions are absent, incorporating the nine
highest-risk scenarios for each machine. In the process of developing the scenarios, the o3 and
o1 models from the AI-driven ChatGPT program, known for their highest accuracy rates, were
utilized, which increased the reliability of the scenarios and outcomes. Following the risk
assessment, the program presents the total risk score and body-region-specific risk levels as
probability percentages while also providing users with risk mitigation strategies, preventive
measures, and potential repercussions of non-compliance to enhance workplace safety and
reduce individual hazards. Initially implemented in the construction sector, the developed
system has the scalability to be adapted across various industries, including manufacturing,
healthcare, corporate environments, transportation and warehousing.