Published

2024-05-29

A Novel Global Probabilistic Fuzzy System for Occupational Risk Assessment (GPFSORA)

Un novedoso sistema probabilístico difuso global para la evaluación de riesgos laborales (GPFSORA)

DOI:

https://doi.org/10.15446/ing.investig.104181

Keywords:

probabilistic fuzzy system, frequentist probability, total probability theorem (en)
sistema probabilístico difuso, probabilidad frecuentista, teorema de probabilidad total (es)

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Occupational risk assessment is the process of estimating the magnitude of risks that cannot be avoided. Then, the corresponding assessment is carried out, using comparative tables with different evaluation methods. Current risk assessment techniques enable the individual assessment of each potential risk, but there is no method to globally assess potential risks in an organization. The motivation of this research was to develop an objective and quantitative risk assessment system through a diffuse probabilistic model integrating stochastic and non-stochastic uncertainty. To this effect, an empirical collective record was used, whose attribute of interest was the occurrence of different accident types over a period of 52 weeks. Here, each of the collectives represented a linguistic input variable. In the probabilistic fuzzification stage, the frequentist probability of the occurrence of accidents was determined. One of our most important contributions to probabilistic fuzzy systems lies in our classification of language labels based on the linguistic projection of frequentist probabilities via a projection membership function determined by experts. The use of the total probability theorem in the implication stage is also proposed. The output of the system determines the type of risk, its evaluation, and the probability of its occurrence, vital factors to be considered in prevention work. The system’s stages are explicitly described and applied to real data corresponding to construction materials distribution company. One of the relevant conclusions of this research is that the integration of stochastic and imprecise uncertainty allows for a more reliable risk assessment system.

La evaluación de riesgos laborales es el proceso de estimar la magnitud de los riesgos que no se pueden evitar. Luego, se lleva a cabo la evaluación correspondiente, utilizando tablas comparativas con diferentes métodos de evaluación. Las técnicas actuales de evaluación de riesgos permiten la evaluación individual de cada riesgo potencial, pero no hay un método para evaluar globalmente los riesgos potenciales en una organización. La motivación de esta investigación fue desarrollar un sistema objetivo y cuantitativo de evaluación de riesgos a través de un modelo probabilístico difuso que integrara la incertidumbre estocástica y no estocástica. Para ello, se utilizó un registro colectivo empírico, cuyo atributo de interés fue la ocurrencia de diferentes tipos de accidentes durante un período de 52 semanas. Aquí, cada uno de los colectivos representaba una variable de entrada lingüística. En la etapa de difusión probabilística, se determinó la probabilidad frecuentista de la ocurrencia de accidentes. Una de nuestras contribuciones más importantes a los sistemas difusos probabilísticos radica en la clasificación de etiquetas de lenguaje con base en la proyección lingüística de probabilidades frecuentistas a través de una función de membresía de proyección determinada por expertos. También se propone el uso del teorema de probabilidad total en la etapa de implicación. La salida del sistema determina el tipo de riesgo, su evaluación y la probabilidad de su ocurrencia, factores vitales a tener en cuenta en el trabajo de prevención. Las etapas del sistema se describen explícitamente y se aplican a datos reales de una empresa de distribución de materiales de construcción. Una de las conclusiones relevantes de esta investigación es que integrar incertidumbre estocástica e imprecisa permite un sistema de evaluación de riesgos más confiable.

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How to Cite

APA

Baeza Serrato, R. (2024). A Novel Global Probabilistic Fuzzy System for Occupational Risk Assessment (GPFSORA). Ingeniería e Investigación, 44(2), e104181. https://doi.org/10.15446/ing.investig.104181

ACM

[1]
Baeza Serrato, R. 2024. A Novel Global Probabilistic Fuzzy System for Occupational Risk Assessment (GPFSORA). Ingeniería e Investigación. 44, 2 (Feb. 2024), e104181. DOI:https://doi.org/10.15446/ing.investig.104181.

ACS

(1)
Baeza Serrato, R. A Novel Global Probabilistic Fuzzy System for Occupational Risk Assessment (GPFSORA). Ing. Inv. 2024, 44, e104181.

ABNT

BAEZA SERRATO, R. A Novel Global Probabilistic Fuzzy System for Occupational Risk Assessment (GPFSORA). Ingeniería e Investigación, [S. l.], v. 44, n. 2, p. e104181, 2024. DOI: 10.15446/ing.investig.104181. Disponível em: https://revistas.unal.edu.co/index.php/ingeinv/article/view/104181. Acesso em: 3 feb. 2025.

Chicago

Baeza Serrato, Roberto. 2024. “A Novel Global Probabilistic Fuzzy System for Occupational Risk Assessment (GPFSORA)”. Ingeniería E Investigación 44 (2):e104181. https://doi.org/10.15446/ing.investig.104181.

Harvard

Baeza Serrato, R. (2024) “A Novel Global Probabilistic Fuzzy System for Occupational Risk Assessment (GPFSORA)”, Ingeniería e Investigación, 44(2), p. e104181. doi: 10.15446/ing.investig.104181.

IEEE

[1]
R. Baeza Serrato, “A Novel Global Probabilistic Fuzzy System for Occupational Risk Assessment (GPFSORA)”, Ing. Inv., vol. 44, no. 2, p. e104181, Feb. 2024.

MLA

Baeza Serrato, R. “A Novel Global Probabilistic Fuzzy System for Occupational Risk Assessment (GPFSORA)”. Ingeniería e Investigación, vol. 44, no. 2, Feb. 2024, p. e104181, doi:10.15446/ing.investig.104181.

Turabian

Baeza Serrato, Roberto. “A Novel Global Probabilistic Fuzzy System for Occupational Risk Assessment (GPFSORA)”. Ingeniería e Investigación 44, no. 2 (February 20, 2024): e104181. Accessed February 3, 2025. https://revistas.unal.edu.co/index.php/ingeinv/article/view/104181.

Vancouver

1.
Baeza Serrato R. A Novel Global Probabilistic Fuzzy System for Occupational Risk Assessment (GPFSORA). Ing. Inv. [Internet]. 2024 Feb. 20 [cited 2025 Feb. 3];44(2):e104181. Available from: https://revistas.unal.edu.co/index.php/ingeinv/article/view/104181

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