Safety of Workers in Indian Mines: Study, Analysis, and Prediction

Abstract

BackgroundThe mining industry is known worldwide for its highly risky and hazardous working environment. Technological advancement in ore extraction techniques for proliferation of production levels has caused further concern for safety in this industry. Research so far in the area of safety has revealed that the majority of incidents in hazardous industry take place because of human error, the control of which would enhance safety levels in working sites to a considerable extent.MethodsThe present work focuses upon the analysis of human factors such as unsafe acts, preconditions for unsafe acts, unsafe leadership, and organizational influences. A modified human factor analysis and classification system (HFACS) was adopted and an accident predictive fuzzy reasoning approach (FRA)-based system was developed to predict the likelihood of accidents for manganese mines in India, using analysis of factors such as age, experience of worker, shift of work, etc.ResultsThe outcome of the analysis indicated that skill-based errors are most critical and require immediate attention for mitigation. The FRA-based accident prediction system developed gives an outcome as an indicative risk score associated with the identified accident-prone situation, based upon which a suitable plan for mitigation can be developed.ConclusionUnsafe acts of the worker are the most critical human factors identified to be controlled on priority basis. A significant association of factors (namely age, experience of the worker, and shift of work) with unsafe acts performed by the operator is identified based upon which the FRA-based accident prediction model is proposed.

What do applications of systems thinking accident analysis methods tell us about accident causation? A systematic review of applications between 1990 and 2018

“…In terms of HFACS framework modifications, eight studies incorporated an additional fifth level above the organisational level (Reinach and Viale, 2006;Patterson and Shappell, 2010;Chauvin et al, 2013;Chen et al, 2013;Theophilus et al, 2017;Verma and Chaudhari, 2017;Yıldırım et al, 2017;Zhang et al, 2018). Thirty-four studies used a traditional four level HFACS framework that included between 17 and 20 individual categories.…”

Organizational Factors and Risk Management in the Mining Industry

“…Taking each shape to represent studies that featured some form of relationship between organizational factors and the variable, it is evident that about two-thirds of the reviewed studies acknowledged a linkage between organizational factors and accident causation. In particular, six studies (Aliabadi et al, 2018;Lenné et al, 2012;Patterson & Shappell, 2010;Verma & Chaudhari, 2017;Yulianto, Haramaini, & Siregar, 2015;Yunxiao & Yangke, 2014) investigated accident causation using HF tools such as the human factors analysis and classification system (HFACS) developed by Wiegmann and Shappell (2007), including a modified (HFACS-MI) tailored for the mining industry. The major outcomes of the accident analyses using the HFACS tool included the identification of organizational factors as causes or contributory factors of accidents in the mining industry.…”

Data digitalisation in the mining industry – a scoping review protocol

“…However, this new technological era is important not only to increase production rates, but also to improve safety conditions. Despite the progress in the occupational health and safety field, the mining industry is still seen as a high-risk occupation (Nowrouzi-Kia et al, 2018;Verma & Chaudhari, 2017). Although accidents affect the workers and their families directly, they can also be considered a burden on the company, once they may entail significant costs deriving from property and equipment damage, as well as production losses (Ural & Demirkol, 2008).…”