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Technical Brief

A Fuzzy Logic Approach in the Definition of Risk Acceptance Boundaries in Occupational Safety and Health

[+] Author and Article Information
Matilde A. Rodrigues

Research Centre on Environment and Health, School of Allied Health Technology of Institute Polytechnic of Porto,
Vila Nova de Gaia 4400-330, Portugal
e-mail: mar@estsp.ipp.pt

Celina P. Leão

Mem. ASME DPS/ALGORITMI Research Centre, School of Engineering, University of Minho,
Guimarães, 4800-058, Portugal
e-mail: cpl@dps.uminho.pt

Eusébio Nunes

DPS/ALGORITMI Research Centre, School of Engineering, University of Minho,
Guimarães, 4800-058, Portugal
e-mail: enunes@dps.uminho.pt

Sérgio Sousa

DPS/ALGORITMI Research Centre, School of Engineering, University of Minho,
Guimarães, 4800-058, Portugal
e-mail: sds@dps.uminho.pt

Pedro Arezes

DPS/ALGORITMI Research Centre, School of Engineering, University of Minho,
Guimarães, 4800-058, Portugal
e-mail: parezes@dps.uminho.pt

Manuscript received February 24, 2015; final manuscript received March 3, 2016; published online August 19, 2016. Assoc. Editor: Chimba Mkandawire.

ASME J. Risk Uncertainty Part B 2(4), 044501 (Aug 19, 2016) (6 pages) Paper No: RISK-15-1031; doi: 10.1115/1.4032923 History: Received February 24, 2015; Accepted March 03, 2016

Organizations need to make decisions about risk acceptance, to decide about the need of risk-reducing measures. In this process, the personal judgments of occupational safety and health (OSH) practitioners have great importance. If on one hand, they have the technical knowledge about risk; on the other hand, the decisions can be dependent on their level of risk acceptance. This paper analyzes judgments of OSH practitioners about the level of risk acceptance, using the fuzzy logic approach. A questionnaire to analyze the reported level of risk acceptance was applied. The questionnaire included 79 risk scenarios, each accounting for the frequency of an accident with more lost workdays than a given magnitude. Through the two-step cluster analysis, three groups of OSH practitioners were identified: unacceptable, tolerable, and realistic groups. A further analysis of the realistic group judgments about risk was performed, using the fuzzy logic approach. The fuzzy sets of input and output variables were determined, and the relationship between the variables was mapped through fuzzy rules. After that, the min–max fuzzy inference method was used. The obtained results show that the risk level is acceptable when input variables are at the lowest value and unacceptable when the risk level is high. The obtained results allow us to better understand the modeling of OSH practitioners’ judgments about risk acceptance, noting the uncertainty related to these judgments.

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Figures

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Fig. 1

Example of the scenarios included in the questionnaire

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Fig. 2

Graphical representation (membership functions) of fuzzy variable days lost

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Fig. 3

Graphical representation (membership functions) of fuzzy variable accident frequency

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Fig. 4

Fuzzy graphical representation for the risk level of the occupational accidents

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Fig. 5

IF–THEN rules for risk level inference by changing the values of inputs

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Fig. 6

Fuzzy function defined by IF–THEN rules between two inputs (days lost and accident frequency) and output (risk level) in a three-dimensional input–output space

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Fig. 7

Fuzzy function defined by IF–THEN rules between two inputs (days lost and accident frequency) and output (risk level) in a 2D input–output space

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