Assessment of workplace accident risks in underground collieries by integrating a multi-goal cause-and-effect analysis method with MCDM sensitivity analysis

Bakhtavar, Ezzeddin; Yousefi, Samuel

doi:10.1007/s00477-018-1618-x

Cited by 44 publications

(21 citation statements)

References 60 publications

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“…Due to the inherent uncertainty of these projects, they utilized FCM including risk parameters to deal with such uncertainties and to assess the highway cost-benefit analysis. Bakhtavar and Yousefi [22] studied workplace accidents in the mining industry and proposed an integrated approach using multi-goal FCM and technique for order of preference by similarity to ideal solution (TOPSIS). They analyzed underground collieries accidents using FCM based on multiple goals and risk effects and did a sensitive analysis by TOPSIS to improve the safety of the workplace.…”

Section: Discussionmentioning

confidence: 99%

“…Furthermore, the capabilities of this method, such as the ability to model complex systems with limited and missing data or in the situation in which the data collection process is expensive, has increased its application domain [103]. Although this subject has been introduced in the field of psychology, its applications have been developed in other disciplines such as geography [87], education [37], systems control [51,52], transportation [18], medicine [10], supply chain [6], banking [17], engineering [22], energy [5], and environment [117].…”

Section: Fuzzy Cognitive Mapsmentioning

confidence: 99%

“…As reviewed in this study, FCMs can be used to study the impact of each strategy on the system status in the forms of modeling, analysis, prediction, systematic learning, and classification. To this end, some concepts can be considered evaluator nodes using multi-objective fuzzy cognitive maps [22], based on management's discretion or organizational goals. In this way, risks that are directly affected are considered active nodes and can be determined after running each scenario as a solution.…”

Section: Future Trendsmentioning

confidence: 99%

See 2 more Smart Citations

Fuzzy cognitive maps in systems risk analysis: a comprehensive review

Bakhtavar

Valipour

Yousefi

et al. 2020

Complex Intell. Syst.

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Fuzzy cognitive maps (FCMs) have been widely applied to analyze complex, causal-based systems in terms of modeling, decision making, analysis, prediction, classification, etc. This study reviews the applications and trends of FCMs in the field of systems risk analysis to the end of August 2020. To this end, the concepts of failure, accident, incident, hazard, risk, error, and fault are focused in the context of the conventional risks of the systems. After reviewing risk-based articles, a bibliographic study of the reviewed articles was carried out. The survey indicated that the main applications of FCMs in the systems risk field were in management sciences, engineering sciences and industrial applications, and medical and biological sciences. A general trend for potential FCMs’ applications in the systems risk field is provided by discussing the results obtained from different parts of the survey study.

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Section: Discussionmentioning

confidence: 99%

Section: Fuzzy Cognitive Mapsmentioning

confidence: 99%

Section: Future Trendsmentioning

confidence: 99%

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Fuzzy cognitive maps in systems risk analysis: a comprehensive review

Bakhtavar

Valipour

Yousefi

et al. 2020

Complex Intell. Syst.

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“…Therefore, many researchers are using soft computing as a powerful alternative tool in solving these problems [1][2][3][4][5][6]. The risk assessment of civil and industrial projects is one of the most attractive and practical engineering problems and is inherently uncertain and complex [7][8][9][10][11][12][13][14]. Some researches were conducted on the tunneling projects' risk using different methods such as Fault Tree, Even Tree and Monte Carlo Simulation [15][16][17][18][19].…”

Section: Introductionmentioning

confidence: 99%

Assessment of risks of tunneling project in Iran using artificial bee colony algorithm

et al. 2019

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The soft computing techniques have been widely applied to model and analyze the complex and uncertain problems. This paper aims to develop a novel model for the risk assessment of tunneling projects using artificial bee colony algorithm. To this end, the risk of the second part of the Emamzade Hashem tunnel was assessed and analyzed in seven sections after testing geotechnical characteristics. Five geotechnical and hydrological properties of study zone are considered for the clustering of geological units in front of tunneling project including length of tunnel, uniaxial compressive strength, rock mass rating, tunneling index Q, density and underground water condition. These sections were classified in two lowrisk and high-risk groups based on their geotechnical characteristics and using clustering technique. It was resulted that three sections with lithologies Durood Formation, Mobarak Formation, and Ruteh Formation are placed in the high risk group and the other sections with lithologies Baroot Formation, Elika Formation, Dacite tuff of Eocene, and Shear Tuff, and Lava Eocene are placed in the low risk group. In addition, the underground water condition and density with 0.722 and 1 Euclidean distances have the highest and lowest impacts in the high risk group, respectively. Therefore, comparing the obtained results of modelling and actual excavation data demonstrated that this technique can be applied as a powerful tool for modeling risks of tunnel and underground constructions.

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“…Limited research has been reported on the applications of FCM in mining and rock engineering problems. Bakhtavar and Yousefi (2018) integrated FCM based on a multi-goal concept and a sensitivity analysis in the TOPSIS process to prioritize workplace accident risks in underground coal mines. Bakhtava, Shahmoradi, and Rahmati (2018) used an FCM to evaluate the effective factors that give rise to occupational risks in underground coal mines in Iran by analysing the cause and effect interactions among the identified factors.…”

Section: Introductionmentioning

confidence: 99%

Evaluation of shaft locations in underground mines: Fuzzy multi-objective optimization by ratio analysis with fuzzy cognitive map weights

Bakhtavar

Yousefi

Jafarpour

2019

J. S. Afr. Inst. Min. Metall.

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The determination of the location of production and ventilation shafts is one of the most important issues in underground mines from both a technical and an economic viewpoint. This study introduces an integrated approach using fuzzy cognitive map (FCM) and fuzzy multi-objective optimization by ratio analysis (FMOORA) to evaluate several candidate shaft locations and consequently increase ore production from underground mines. The FCM based on a hybrid learning algorithm was applied to analyse interactions and internal relationships between criteria and to find complex causal relationships between different factors. After importing the weights resulting from the FCM into the FMOORA, shaft alternatives were evaluated and prioritized. An iron ore case example was evaluated by the integrated approach. Results of the integrated approach were validated by the results obtained from fuzzy-TOPSIS and on-site evaluations by mining experts.

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Assessment of workplace accident risks in underground collieries by integrating a multi-goal cause-and-effect analysis method with MCDM sensitivity analysis

Cited by 44 publications

References 60 publications

Fuzzy cognitive maps in systems risk analysis: a comprehensive review

Fuzzy cognitive maps in systems risk analysis: a comprehensive review

Assessment of risks of tunneling project in Iran using artificial bee colony algorithm

Evaluation of shaft locations in underground mines: Fuzzy multi-objective optimization by ratio analysis with fuzzy cognitive map weights

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