Interval type-2 fuzzy logic and its application to occupational safety risk performance in industries
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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.
KeywordsSafety performance Expert system Construction safety Risk assessment Fuzzy inference system Interval type-2 fuzzy logic
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).
Compliance with ethical standards
Conflict of interest
The authors have no conflict of interest for the publication of this paper.
The authors declared that this article does not contain any studies with human participants or animals performed by any of the authors.
- Alavi N (2013) Quality determination of Mozafati dates using Mamdani fuzzy inference system. J Saudi Soc Agric Sci 12:137–142Google Scholar
- Castillo O, Melin P (2008) Type-2 fuzzy logic: theory and applications. In: Studies in fuzziness and soft computing. Springer, Berlin, p 223Google 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 38:14148–14155Google 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 80:299–311Google Scholar
- 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):110–126Google Scholar
- 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 73:183–192Google Scholar
- 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 UniversityGoogle 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 347–361Google Scholar