Safety sampling on drivers of mine vehicles in cements companies.

Abstract

BackgroundCement companies supply construction sites, and the mining industry relies on heavy equipment such as cranes, excavators, and dump trucks. These vehicles pose risks of severe injuries due to heavy machinery operations, high-energy movements, and complex environments, leading to collisions, rollovers, mechanical failures, and human error.ObjectiveThis descriptive-analytical study examined the relationship between unsafe behaviors and workplace conditions among 40 mining machinery drivers in three Kerman cement factories.MethodsSafety behavior sampling (SBS) was used to measure unsafe acts through defining behaviors, training observers, and random observations. The sample size followed established formulas. Data were analyzed using SPSS 21, applying descriptive statistics, the Kolmogorov-Smirnov normality test, and correlation analyses to assess relationships between unsafe behaviors and factors including age, marital status, work experience, employment type, safety training, vehicle type, and environmental noise and vibration.ResultsFrom 421 observed activities, significant correlations emerged between unsafe behaviors and driver age (p = 0.001), work experience (p = 0.0001), education (p = 0.0495), employment status (p = 0.003), observation time (p = 0.0001), safety training (p = 0.02), car model (p = 0.0001), driving license (p = 0.0001), and vehicle inspection (p = 0.003). Additionally, unsafe behaviors were positively correlated with noise (p < 0.05) and vibration levels (p = 0.008).ConclusionsNoise levels strongly correlate with unsafe behaviors among mining vehicle drivers, emphasizing the importance of environmental factors. Integrated approaches involving driver training and noise reduction are essential to enhance occupational safety.