A Novel Human Error Probability Assessment Using Fuzzy Modeling

Abstract

Human error is one of the significant factors contributing to accidents. Traditional human error probability (HEP) studies based on fuzzy number concepts are one of the contributions addressing such a problem. It is particularly useful under circumstances where the lack of data exists. However, the degree of the discriminability of such studies may be questioned when applied under circumstances where experts have adequate information and specific values can be determined in the abscissa of the membership function of linguistic terms, that is, the fuzzy data of each scenario considered are close to each other. In this article, a novel HEP assessment aimed at solving such a difficulty is proposed. Under the framework, the fuzzy data are equipped with linguistic terms and membership values. By establishing a rule base for data combination, followed by the defuzzification and HEP transformation processes, the HEP results can be acquired. The methodology is first examined using a test case consisting of three different scenarios of which the fuzzy data are close to each other. The results generated are compared with the outcomes produced from the traditional fuzzy HEP studies using the same test case. It is concluded that the methodology proposed in this study has a higher degree of the discriminability and is capable of providing more reasonable results. Furthermore, in situations where the lack of data exists, the proposed approach is also capable of providing the range of the HEP results based on different risk viewpoints arbitrarily established as illustrated using a real-world example.

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“…Ferdous et al found that trapezoidal and triangular fuzzy numbers form a sound practical alternative to reflect uncertainties and fuzziness of human justifications. These two types of fuzzy numbers have a smooth transition from one linguistic value to another value …”

“…These two types of fuzzy numbers have a smooth transition from one linguistic value to another value. [25] Basic principle and definition of fuzzy probability are briefly introduced below. More details can be found in reference by Li.…”

Applications of fuzzy faulty tree analysis and expert elicitation for evaluation of risks in LPG refuelling station

“…Since previous researchers confirm that trapezoidal and triangular fuzzy numbers form a sound practical alternative to reflect uncertainties, inaccuracy and fuzziness of human justifications involving in linguistic values (Wolkenhauer, 2001;Ferdous et al, 2011) and have smooth transitions from one linguistic term to another term (Ung and Shen, 2011). These types of membership functions are considered in this study to represent basic event failure possibilities.…”

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