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Showing 2 results for Ghiyasi
Analysis of Occupational Accidents Based on the Human Factors Analysis and Classification System (HFACS): A Case Study in a Copper Mine
Amin Amiri Ebrahimabadi, Ahmad Soltanzadeh, Samira Ghiyasi,
Volume 8, Issue 1 (Iranian Journal of Ergonomics 2020)
Abstract

Background and Aim: Occupational accidents are recognized as one of the major concerns in the mining industry. The purpose of this study was to analyze the incidence of occupational accidents in a mine for 10 years using Human Factor Analysis and Classification System (HFACS).
Method: This cross-sectional study was carried out on 664 mining accidents during 2009-2018. The tools used in this study included accident reporting checklists, human factors analysis and classification system (HFACS), and a team approach to analyze these accidents. Data analysis was performed using IBM SPSS AMOS v. 23.0.
Results: The accident frequency rate (AFR) was 15.10±3.34. The results of 10-years accident analysis in this mine based on HFACS model showed that the highest contribution of each parameter to the four layers including unsafe acts, preconditions for unsafe acts, unsafe supervision and organizational influences were respectively devoted to perceptual error (64.4%), Physical environment (29.5%), inadequate supervision (59.6%), and organizational process (65.6%). The results of structural equation modeling showed that the AFR is directly and indirectly affected by the layers of the HFACS model (P<0.05). The most significant impact on the AFR was related to the unsafe acts layer.
Conclusion: The findings of this study indicated that all four causal layers of human factors were effective in mine accidents, in addition the HFACS model is highly effective for unsafe acts-based accidents analysis, so it can be used for future planning to reduce accidents in the mining sector.


Identification and Evaluation of Human Errors in Locomotive Maneuvers Using the SHERPA Technique: A Case Study in a Rail Operation Repair and Development Project
Fatemeh Karami, Samira Ghiyasi, Ahmad Soltanzadeh,
Volume 8, Issue 4 (Iranian Journal of Ergonomics 2021)
Abstract
Background and Objectives: ِِDespite complex technologies in many work environments, human errors are of great importance as they might lead to severe and catastrophic accidents. Therefore, in order to prevent and limit the consequences of human error, it seems necessary to identify and find the causes of them. The aim of this study was to identify and evaluate the human errors of locomotive maneuvers in the railway repair and development project, 2019.
Methods: In this cross-sectional study, the identification and evaluation of human errors in locomotive maneuvers in the MAPNA railway repair and development project using SHERPA technique was done. First, using the hierarchical task analysis method, the activities of the maneuvers are divided into their tasks and sub-tasks; in the next step, the types of human errors in each of the tasks were identified and then human errors were evaluated according to SHERPA instruction.
Results: A total of 206 errors were identified in the present study. Errors included 48.5% action error, 39.8% checking error, 10.2% information communication error and 1.5% selection error. The lowest and highest errors related to locomotive displacement error were related to hot single diesel (14.0%) and locomotive displacement error on service pit (29.6%). Of the identified errors, 23.8%  had an unacceptable risk level, 51.1% had an ALARP risk level and 25.2% had an acceptable risk level.
Conclusion: The findings of the study indicated that the most unacceptable risks and ALARP were related to checking and action error, respectively. So, it is suggested that the design and implementation of control measures related to these two types of errors should be prioritized.
 

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