The 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.
Methods
The 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.
Results
The 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.
Conclusion
Unsafe 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.
- Materials and methods
2.1. Data
The accident data reports, summary sheets, and narratives referred for analysis were gathered from one of the major manganese ore extraction central government undertaking companies with four mining sites in Maharashtra and six mining sites in Madhya Pradesh in India. Accident data spanning from 1985 to 2015 was referred for analysis with a total of 119 case histories. Among these, 17 cases were found to be partially documented and were discarded; the remaining 102 cases were finally considered. For accidents leading to fatalities the reports were retrieved from the Directorate General of Mining Safety, because such reports were submitted to the central body in consideration of the severity of the outcome. The forms and reports referred were in standard format and uniform, as required by the Directorate General of Mining Safety. The data combined both underground and open-cast mines.
2.2. Coding process
One human factors specialist along with three seasoned experts with nearly 40 years of experience in the industry, analyzed, coded the cases, and categorized human factors. Because there was one rater the consensus classification was deemed appropriate for the analysis and the concern regarding interrater reliability was insignificant. Incidences were analyzed for each category of the HFACS framework for coding.
Preconditions for unsafe acts is further classified into environmental (physical and technical environment), operator’s condition, and personnel factors. The mining industry is known for its dynamic and difficult environmental conditions. Issues concerned with illumination, ventilation, etc., have been a hurdle in maintaining safety at the worksite. Technical environmental factors were found to be responsible in 38 cases. Condition and maintenance of tool and operations related to tools and equipment (36.85%) were identified as being mostly responsible for mishaps; standard operating procedures (SOPs) and risk assessment were lower (10.5%) because the mines are semimechanized, so issues related to noncompliance or violating SOPs are very low and most risks associated with faulty machines are quickly assessed and handled cautiously. Under physical environment, weather is also an important factor, but it has not contributed to a great extent in leading mishaps. The rainy season is the most concerning environmental condition for the mining industry and specifically for open-cast mines. During this season, the mining site is drowned which obstructs work. Interaction of such hazardous site conditions and workers is limited, which helps to prohibit accidents; pumps are used to remove water from the site until the sites are accessible for working.
The physical environment was found to be responsible in 22 cases. Surface/road conditions (27.27%) followed by visibility (18.18%) was found to be dominant. The contribution of ergonomics was identified as being insignificant because the mines are semimechanized, so uncomfortable, unsuitable man–machine interaction or faulty workplace design is not noticed.
Condition of the operator was found to be responsible in 10 cases. Still, it is a very important factor to be considered because an operator with poor mental health will definitely underperform the task, which might lead to unsafe consequences and also poor productivity. Physical/mental limitations and adverse physiological state (40.02%) was noticed to be the priority as a causal factor for accidents under this category. Under the category of physical/mental limitations, learning ability limitations were found to be responsible to a greater extent, followed by condition-based respiratory issues; the rest of the factors were not considerably noticed, because height, weight, hearing capability, and vision tests are done prior to the worker joining, which they have to clear mandatorily. If the worker eventually develops any limitation during his work then he is assigned light duty, for example, medical attendant on site, peon in office, store, etc.
With analysis, personnel factor was found to be responsible in 28 cases. It was found that the contribution of communication and coordination (64.29%) is of topmost priority, followed by fitness for duty (35.70%).
3.3. Unsafe leadership
The role of the leader is to provide adequate training and guidance to the team members to perform any task/operation efficiently and safely. In the absence of adequate leadership or leadership violations etc., unwanted consequences can come into existence. This category is further subdivided into inadequate leadership (22.55%) which was a major causal factor in incidents, followed by planned inappropriate operations (7.84%), failure to correct known problems (8.21%), and leadership violations (5.38%). As expected, leadership violations were the lowest. Under the category of inadequate leadership, training-related issues showed a major contribution (39.13%). At times it happens that less than adequate training is given to the worker to perform the task; there are a variety of mandatory trainings in order to work on a mining site, such as when there is a change in SOP, refresher training, etc.
Alternatively, if the worker does not have competency to learn this can also create problems related to an unsafe working environment. Safety oversight (30.4%) had the second highest contribution in mishaps. The analysis showed that safety regulatory requirements were still not set, yet the operator was permitted to continue working which led to mishap. The timber that is used to provide roof support in underground mines has a certain specification which has to be followed, the material winding in any situation should not be used for movement of man and material together, no matter how heavy cap-lamp batteries are, it has to be carried in underground.
If any kind of deviation is noticed in following such practices, an efficient leader should take immediate action to avoid any mishap. In some of the cases this was missing, leading to issues related to safety oversight.
It was noticed that in emergency circumstances, certain decisions were taken which are unconventional during normal situations/ operations. The execution of such decisions with poor plan formulation will never result in the intended manner, which was also found in the analysis. Major causal factors under planned inappropriate operation were improper task or work plan (50.28%), followed by the work assignment (25.14%) nanocode. If a blaster is not available and there is an emergency, a worker who has not done blasting before cannot be assigned with the task of blasting, or a driver who has never operated or driven heavy earth moving machinery before, but has been driving jeeps/ambulances on site should not be allowed to drive loaders, dumpers, or tippers under emergency conditions. Anyone who is in job rotation and has handled such machinery can be assigned tasks during an emergency situation to avoid accidents. If an improper work assignment is made then that might lead to unfavorable events. Leadership violation was found to be negligibly responsible. Another inference can be drawn from this: leadership violation might have been responsible, but not documented or reported to overcome the drastic after effects upon the employment of the personnel responsible or vigilance inquiry issues.
3.4. Organizational influences
In a total of 14 cases, organizational factors were found to be responsible. Organizational process (42.64%) was identified as the dominant factor. Irregular reporting was found to create issues in the cases analyzed. Time pressure and shortage of staff were other important identified causal factors. As far as outside factors are concerned, these were not identified when analyzing cases. One of the reasons could be that documentation provided for analysis did not describe any outside factor responsible in mishaps.
- Fuzzy reasoning approach
As discussed in previous sections, if any significant association exists between factors such as age of the worker, place of work, shift, or experience, then an FRA model can predict the level of risk associated with the given situation (combination of above-mentioned factors, for example prediction of risk level if “a worker of age 27 years, with 1 year of experience, working in the third shift i.e., night shift underground”). So that once the risk level can be predicted for a given situation and if a considerable risk level is reflected than changes such as in time, place, or nature of work can be made, the level of risk can be rechecked, and finally the allocation of work can be made. This can enhance preparedness against unsafe consequences and a safe working environment can be developed and maintained in the future. The outcome of the significance testing indicated a significant association between unsafe acts of the worker with the age, shift, and experience of the worker. Considering the same, and with the help of three experts with 40 years of experience in this field, a fuzzy rule base was prepared to develop in the inference engine so that risk level can be assessed.
A fuzzy set can be defined as: A fuzzy subset A of a universe of discourse U is characterized by a membership function μ: U→ (0, 1) which associates with each element u of U a number μ (u) in the interval (0, 1) which represents the grade of membership of u in A. The fuzzy set A of U = u1, u2 ….. u, will be denoted:(1)A= i=1nμA(ui) /ui=∑tμA(ui),where Σ stands for the union [26].
A fuzzy number can be demonstrated with an example of the triangular fuzzy number, given as;(2)ñña(tlatmatua)and can be interpreted as [43]:(3)ñμña(x)={0,x<tlax−tlatma−tlatla≤x≤tmatua−xtua−tmatma≤x≤tua0,x>tua
The proposed FRA model was developed using MATLAB R2009a, Fuzzy Logic tool box. The FRA model is used where only a small portion of the knowledge (information) for a typical problem might be regarded as certain or deterministic. The FRA model was developed with the following steps.
4.1. Fuzzy inputs
Fuzzy inputs need to be crisp numerical values limited to the universe of discourse of the input variable. The degree to which the input belong to appropriate fuzzy sets is decided through a membership function, which is one of the critical steps in deciding and defining inputs. The output is a fuzzy degree of membership between 0 and 1.
4.2. Application of fuzzy operator
Once the inputs are fuzzified, the degree to which each part of the antecedent is satisfied for each rule is identified. The output is always a single truth value, but if there is more than one part in the antecedent, the fuzzy operator is applied to get one number that represents the result of antecedent, of that rule which is applied to the output function.
4.3. Implication
To shape up the consequent implication method is applied. Implication occurs for each rule, the number given by the antecedent is the input for implication. Each rule has a weight which is applied to the number given by the antecedent. Normally it takes 1 and it does not affect the implication process, this number may be varied from time to time from 1 in order to weigh one rule relative to another.
4.4. Aggregation
All the fuzzy sets representing the output of each rule are combined to a single fuzzy set. Aggregation occurs once for each output variable. The input for the aggregation process is the list of truncated output functions returned by the implication process for each rule. The output of the aggregation process is one fuzzy set for each output variable.
4.5. Defuzzification
The input given to the fuzzy reasoning system is crisp, similarly the output is also expected in crisp form. The defuzzification process gives a crisp form of output. The aggregate output fuzzy set is the input for this step and the output is crisp in nature.
Conclusion
The work presents a detailed analysis of mine accidents in underground as well as open-cast manganese mines in India. The HFACS framework was adopted to perform the analysis and significant findings were obtained. Based upon the findings an FRA model was proposed to assess the risk level for a given situation and modify the same if found critical. The outcome of the research work is highlighted as follows. (1) Unsafe acts of worker found to be the most critical factor in the development of accident scenarios in mining sites, with a maximum contribution of skill-based errors performed by the workers. (2) Underground mining approach, stopping area, I shift of work, worker within the age group of 33–47 years and with 6–10 years of working experience are most critical for consideration in the development of intervention strategies. (3) Faulty behavioral traits and organizational lacunas were indicated as the outcome of HFACAS analysis and can be considered further to develop mitigation plans and intervention strategies for the industry. (4) Age, experience of the worker, and shift of work have a significant correlation with unsafe acts performed, ultimately leading to accidents. (5) The FRA-based risk prediction model proposed can be adopted by the safety analyst to predict the risk associated with a given situation and perform task allocation accordingly to prevent hazardous outcomes.
The present work demonstrates a noble approach to risk and safety assessment. In recent past significant research performed in the area of safety management has been found to be limited with respect to scope because data-based, questionnaire, and interview-based analysis of the data is not performed and the outcome indicated is merely the trend for accidents or reasons behind the mishap. But the present work is a step further in conventional research performed in this area, where the outcome of microlevel accident analysis has been utilized to develop an accident prediction model to interpret the risk levels associated with a given situation and alter them accordingly. In future, the work can further be extended for other minerals extracted for commercial purpose in India and safety levels at sites can be improved.