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2020 | OriginalPaper | Buchkapitel

SCAT Model Based on Bayesian Networks for Lost-Time Accident Prevention and Rate Reduction in Peruvian Mining Operations

verfasst von : Ana Ziegler-Barranco, Luis Mera-Barco, Vidal Aramburu-Rojas, Carlos Raymundo, Nestor Mamani-Macedo, Francisco Dominguez

Erschienen in: Advances in Human Factors, Business Management and Leadership

Verlag: Springer International Publishing

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Abstract

Several factors affect the activities of the mining industry. For example, accident rates are critical because they affect company ratings in the stock market (Standard & Poors). Considering that the corporate image is directly related to its stakeholders, this study conducts an accident analysis using quantitative and qualitative methods. In this way, the contingency rate is controlled, mitigated, and prevented while serving the needs) of the stakeholders. The Bayesian network method contributes to decision-making through a set of variables and the dependency relationships between them, establishing an earlier probability of unknown variables. Bayesian models have different applications, such as diagnosis, classification, and decision, and establish relationships among variables and cause–effect links. This study uses Bayesian inference to identify the various patterns that influence operator accident rates at a contractor mining company, and therefore, study and assess the possible differences in its future operations.

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Metadaten
Titel
SCAT Model Based on Bayesian Networks for Lost-Time Accident Prevention and Rate Reduction in Peruvian Mining Operations
verfasst von
Ana Ziegler-Barranco
Luis Mera-Barco
Vidal Aramburu-Rojas
Carlos Raymundo
Nestor Mamani-Macedo
Francisco Dominguez
Copyright-Jahr
2020
DOI
https://doi.org/10.1007/978-3-030-50791-6_45