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Interval type-2 fuzzy logic and its application to occupational safety risk performance in industries

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Abstract

In this paper, we have developed an interval type-2 fuzzy logic controller  (T2FLC) approach for assessment of the risks that workers expose to at construction sites. Using this novel approach, past accident data, subjective judgments of experts, and the current safety level of a construction site are to be combined. The method is then implemented on a tunneling construction site and risk level for all type of accidents is formulated. In T2FLC assists to trace inputs and outputs in a well-organized manner for building the inferences train so that various types of risk assessment can be predicted in industry. Finally, a comparative study has been successfully performed with type-1 and type-2 fuzzy dataset for improving risk assessment that can be easily determined in the type-2 fuzzy prediction model for improving accuracy. Validity of the proposed model is done with the help of statistical analysis and multiple linear regressions.

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References

  • Abghari SZ, Sadi M (2013) Application of adaptive neuro-fuzzy inference system for the prediction of the yield distribution of the main products in the steam cracking of atmospheric gasoil. J Taiwan Inst Chem Eng 44:365–376

    Article  Google Scholar 

  • Afrinaldi F, Zhang HC (2014) A fuzzy logic based aggregation method for life cycle impact assessment. J Clean Prod 67:159–172

    Article  Google Scholar 

  • Alavi N (2013) Quality determination of Mozafati dates using Mamdani fuzzy inference system. J Saudi Soc Agric Sci 12:137–142

    Google Scholar 

  • Amiryousefi MR, Mohebbi M, Khodaiyan F, Asadi S (2011) An empowered adaptive neuro-fuzzy inference system using selforganizing map clustering to predict mass transfer kinetics in deep-fat frying of ostrich meat plates. Comput Electron Agric 76:89–95

    Article  Google Scholar 

  • Assilian S (1975) An experiment in linguistic synthesis with a fuzzy logic controller. Int J Man Mach Stud 7(1):1–13

    Article  MATH  Google Scholar 

  • Beriha GB, Patnaik B, Mahapatra SS, Padhee S (2012) Assessment of safety performance in Indian industries using fuzzy approach. Expert Syst Appl 39:3311–3323

    Article  Google Scholar 

  • Bevilacquaa M, Ciarapicab FE, Mazzutoa G (2012) Analysis of injury events with fuzzy cognitive maps. J Loss Prev Process Ind 25:677–685

    Article  Google Scholar 

  • Castillo O, Melin P (2008) Type-2 fuzzy logic: theory and applications. In: Studies in fuzziness and soft computing. Springer, Berlin, p 223

  • Castro M, Joao P, Carvalho MCS, Ribeiro JP, Meixedo FJGS (2014) An integrated recycling approach for GFRP pultrusion wastes: recycling and reuse assessment into new composite materials using Fuzzy Boolean Nets. J Clean Prod 66:420–430

    Article  Google Scholar 

  • Chakraborty D, Jana DK, Roy TK (2015) Multi-item integrated supply chain model for deteriorating items with stock dependent demand under fuzzy random and bifuzzy environments. Comput Ind Eng 88:166–180

    Article  Google Scholar 

  • Chen CL, Kaber DB, Dempsey PG (2000) A new approach to applying feed forward neural networks to the prediction of musculoskeletal disorder risk. Appl Ergon 31(3):269–282

    Article  Google Scholar 

  • Ciarapica FE, Giacchetta G (2009) Classification and prediction of occupational injury risk using soft computing techniques: an Italian study. Saf Sci 47(1):36–49

    Article  Google Scholar 

  • Cornelissen AMG, Berg J, Koops WJ, Kaymak U (2003) Elicitation of expert knowledge for fuzzy evaluation of agricultural production systems. Agric Ecosyst Environ 95:1–18

    Article  Google Scholar 

  • Dadeviren M, Yüksel I (2008) Developing a fuzzy analytic hierarchy process (AHP) model for behavior-based safety management. Inf Sci 178:1717–1733

    Article  Google Scholar 

  • Dey S, Jana DK (2015) Application of fuzzy inference system to polypropylene business policy in a petrochemical plant in India. J Clean Prod 112:2953–2968

    Article  Google Scholar 

  • Dutta A, Jana DK (2017) Expectations of the reductions for type-2 trapezoidal fuzzy variables and its application to a multi-objective solid transportation problem via goal programming technique. J Uncertain Anal Appl 5(1):3–15. doi:10.1186/s40467-017-0057-4

    Article  Google Scholar 

  • Ertunc M, Bulgurcu HMH (2011) An adaptive neuro-fuzzy inference system model for predicting the performance of a refrigeration system with a cooling tower. Expert Syst Appl 38:14148–14155

    Google Scholar 

  • Fang DP, Xie F, Huang XY, Li H (2004) Factor analysis-based studies on construction workplace safety management in China. Int J Proj Manag 22(1):3–49

    Article  Google Scholar 

  • Gürcanli GE, Müngen U (2009) An occupational safety risk analysis method at construction sites using fuzzy sets. Int J Ind Ergon 39:371–387

    Article  Google Scholar 

  • Jana DK, Das B, Maiti M (2014) Multi-item partial backlogging inventory models over random planning horizon in random fuzzy environment. Appl Soft Comput 21:12–27

    Article  Google Scholar 

  • Jana DK, Pramanik S, Maiti M (2017a) Mean and CV reduction methods on Gaussian type-2 fuzzy set and its application to a multilevel profit transportation problem in a two-stage supply chain network. Neural Comput Appl 28(9):2703–2726

    Article  Google Scholar 

  • Jana DK, Bej B, Wahab MHA, Mukherjee A (2017b) Novel type-2 fuzzy logic approach for inference of corrosion failure likelihood of oil and gas pipeline industry. Eng Fail Anal 80:299–311

  • Jana DK, Sahoo P, Koczy LT (2017c) Comparative study on credibility measures of type-2 and type-1 fuzzy variables and their application to a multi-objective profit transportation problem via goal programming. Int J Transport Sci Technol 6(2):110–126

  • Khoshnevisan B, Rafiee S, Omid M, Mousazadeh H, Clark S (2014) Environmental impact assessment of tomato and cucumber cultivation in greenhouses using life cycle assessment and adaptive neuro-fuzzy inference system. J clean prod 73:183–192

  • Larcher P, Sohail M (1999) Review of safety in construction and operation for the WS & S sector: part-I. Task No. 166, London School of Hygiene & Tropical Medicine, WEDC, Loughbourough University

  • Larsson TJ, Field B (2002) The distribution of occupational injury risk in the Victorian construction industry. Saf Sci 40(5):439–456

    Article  Google Scholar 

  • Lee CC (1990) Fuzzy logic in control systems: fuzzy logic controller part I and II. IEEE Trans Syst Man Cybern 20:404–435

    Article  MATH  Google Scholar 

  • Mendel JM, John RI, Liu F (2006) Interval type-2 fuzzy logic systems made simple. IEEE Trans Fuzzy Syst 14(6):808–821

    Article  Google Scholar 

  • Naderloo L, Alimardani R, Omid M, Sarmadian F, Javadikia P, Torabi MY, Alimardani F (2012) Application of ANFIS to predict crop yield based on different energy inputs. Measurement 45:1406–1413

    Article  Google Scholar 

  • Olatunji SO, Selamat A, Raheem AA (2014) Improved sensitivity based linear learning method for permeability prediction of carbonate reservoir using interval type-2 fuzzy logic system. Appl Soft Comput 14:144–155

    Article  Google Scholar 

  • Pishgar-Komleh SH, Ghahderijani M, Sefeedpari P (2012) Energy consumption and \({\rm CO}_{2}\) emissions analysis of potato production based on different farm size levels in Iran. J Clean Prod 33:183–191

    Article  Google Scholar 

  • Pramanik S, Jana DK, Mondal SK, Maiti M (2015) A fixed-charge transportation problem in two-stage supply chain network in Gaussian type-2 fuzzy environments. Inf Sci 325:190–214

    Article  MathSciNet  MATH  Google Scholar 

  • Pramanik S, Jana DK, Maiti M (2017) A parametric programming method on Gaussian type-2 fuzzy set and its application to a multilevel supply chain. Int J Uncertain Fuzziness Knowl Based Syst 24(3):451–477

    MathSciNet  MATH  Google Scholar 

  • Sami M, Shiekhdavoodi MJ, Pazhohanniya M, Pazhohanniya F (2014) Environmental comprehensive assessment of agricultural systems at the farm level using fuzzy logic: a case study in cane farms in Iran. Environ Model Softw 58:95–108

    Article  Google Scholar 

  • Sugeno M (1985) Industrial applications of fuzzy control. Elsevier Science Publishing company, Amsterdam, Sole distributors for the U.S.A. and Canada

    MATH  Google Scholar 

  • Valdez F, Melin P, Castillo O (2008) A new evolutionary method combining particle swarm optimization and genetic algorithms using fuzzy logic. In: Soft Computing for Hybrid Intelligent Systems, pp 347–361

  • Wua J, Xionga B, Anb Q, Zhua Q, Lianga L (2015) Measuring the performance of thermal power firms in China via fuzzy Enhanced Russell measure model with undesirable outputs. J Clean Prod 102:237–245

    Article  Google Scholar 

  • Yang Y, Chencheng L, Shiwei JI (2015) Fuzzy multicriteria decision making method based on the improved accuracy function for interval-valued intuitionistic fuzzy sets. Soft Comput. doi:10.1007/s00500-015-1988-7

    MATH  Google Scholar 

  • Zhao H, You JX, Liu HC (2016) Failure mode and effect analysis using MULTIMOORA method with continuous weighted entropy under interval-valued intuitionistic fuzzy environment. Soft Comput. doi:10.1007/s00500-016-2118-x

    Google Scholar 

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Acknowledgements

The authors would like to thank to the editors and anonymous referees for various suggestions which have led to an improvement in both the quality and clarity of the paper. We, Dr. Dipak Kumar Jana and Dr. Sutapa Pramanik, would like to acknowledge the blessings of our daughter Adritya Jana (DOB: 05/10/15).

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Authors and Affiliations

  1. Department of Engineering Science, Haldia Institute of Technology, Haldia, Purba Midnapur, West Bengal, 721657, India

    Dipak Kumar Jana

  2. Department of Applied Mathematics with Oceanology and Computer Programming, Vidyasagar University, Midnapore, West Bengal, 721 102, India

    Sutapa Pramanik

  3. Department of Mathematics, Calcutta Institute of Technology, Banitabla, Uluberia, Howrah, West Bengal, 711316, India

    Palash Sahoo

  4. Department of Chemical Engineering, Haldia Institute of Technology, Haldia, West Bengal, India

    Anupam Mukherjee

Authors
  1. Dipak Kumar Jana
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  2. Sutapa Pramanik
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  3. Palash Sahoo
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  4. Anupam Mukherjee
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Correspondence to Dipak Kumar Jana.

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The authors have no conflict of interest for the publication of this paper.

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The authors declared that this article does not contain any studies with human participants or animals performed by any of the authors.

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Communicated by V. Loia.

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Jana, D.K., Pramanik, S., Sahoo, P. et al. Interval type-2 fuzzy logic and its application to occupational safety risk performance in industries. Soft Comput 23, 557–567 (2019). https://doi.org/10.1007/s00500-017-2860-8

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