ABSTRACT
Background: One of the important actions for enhancing human reliability in any industry is assessing human error probability (HEP). The HEART technique is a robust tool for calculating HEP in various industries. The traditional HEART has some weaknesses due to expert judgment. For these reasons, a hybrid model is presented in this study to integrate HEART with Best-Worst Method. Materials Method: In this study, the blasting process in an iron ore mine was investigated as a case study. The proposed HEART-BWM was used to increase the sensitivity of APOA calculation. Then the HEP was calculated using conventional HEART formula. A consistency ratio was calculated using BWM. Finally, for verification of the HEART-BWM, HEP calculation was done by traditional HEART and HEART-BWM. Results: In the view of determined HEPs, the results showed that the mean of HEP in the blasting of the iron ore process was 2.57E-01. Checking the full blast of all the holes after the blasting sub-task was the most dangerous task due to the highest HEP value, and it was found 9.646E-01. On the other side, obtaining a permit to receive and transport materials was the most reliable task, and the HEP was 8.54E-04. Conclusion: The results showed a good consistency for the proposed technique. Comparing the two techniques confirmed that the BWM makes the traditional HEART faster and more reliable by performing the basic comparisons.
ABSTRACT
Investigation reveals that a high percentage of incident causes are ascribed to some forms of human error. To effectively prevent incidents from happening, Human Reliability Analysis (HRA), as a structured way to represent unintentional operator contribution to system reliability, is a critical issue. Human Error Reduction and Assessment Technique (HEART) as a famous HRA technique, provides a straightforward method to estimate probabilities of human error based on the analysis of tasks. However, it faces varying levels of uncertainty in assigning of weights to each error producing condition (EPC), denoted as assessed proportion of affect (APOA), by experts. To overcome this limitation and consider the confidence level (reliability or credibility) of the experts, the current study aimed at proposing a composite HEART methodology for human error probability (HEP) assessment, which integrates HEART and Z-numbers short for, Z-HEART. The applicability and effectiveness of the Z-HEART has been illustrated in the de-energization power line as a case study. Furthermore, a sensitivity analysis is fulfilled to investigate the validity of the proposed methodology. It can be concluded that Z-HEART is feasible for assessing human error, and despite the methodological contributions, it offers many advantages for electricity distribution companies.
Subject(s)
Accident Prevention/methods , Electric Wiring , Task Performance and Analysis , Humans , Probability , Reproducibility of Results , UncertaintyABSTRACT
Mental Workload (MWL) and human performance are widely contributing concepts in human factors. The objective of the current study is to investigate the perceived MWL and human performance during whole-body vibration (WBV) exposure while seated at different backrest angles. Nineteen healthy male participants completed both the NASA-TLX and rating scale mental effort (RSME) after performing two difficulty levels of computerized dual tasks. The participants' performance was measured in these conditions while seated with a backrest angle of 100 and 120 degrees and exposed to WBV (intensity: 0.5 m/s2; frequency 3-20 Hz) for 5 minutes. No significant effect on performance or perceived MWL (p<0.05) was found when changes were made to the backrest angles. Exposure to WBV under two backrest angles increased mental demand (p=0.04), effort (p=0.03) and frustration (p=0.03) and negatively affected human performance (p<0.05). The present study showed that exposure to WBV could be an important variable for designing work environments that require a high level of performance and mental demand while seated. However, the findings exhibited no association between inclining backrest angle and human performance or perceived MWL.
ABSTRACT
Exposure to extremely low frequency magnetic fields (ELF-MF) and electric shocks occurs in many workplaces and occupations but it is unclear whether any of these exposures cause Amyotrophic lateral sclerosis (ALS). The aim of this systematic review and meta-analysis is to explore whether occupational exposure to ELF-MF and/or electric shocks are risk factor for ALS. We searched PubMed, Embase, and Web of Science databases up to the end of 2019. Pooled risk estimates were calculated using random-effects meta-analysis including exploration of the sources of heterogeneity between studies and publication bias. Twenty-seven publications fulfilled the inclusion criteria. We found a weak, significant, association between occupational exposure to ELF-MF and the risk of ALS (RRPooled estimate: 1.20; 95%CI: 1.05, 1.38) with moderate to high heterogeneity (I2=66.3%) and indication of publication bias (PEgger's test=0.03). No association was observed between occupational exposure to electric shocks and risk of ALS (RRPooled estimate: 0.97; 95%CI: 0.80, 1.17) with high heterogeneity (I2=80.5%), and little indication for publication bias (PEgger's test=0.24). The findings indicate that occupational exposure to ELF-MF, but not electric shocks, might be a risk factor for ALS. However, given the moderate to high heterogeneity and potential publication bias, the results should be interpreted with caution.
