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

2Citations
Citations of this article
6Readers
Mendeley users who have this article in their library.
Get full text

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.

Cite

CITATION STYLE

APA

Ziegler-Barranco, A., Mera-Barco, L., Aramburu-Rojas, V., Raymundo, C., Mamani-Macedo, N., & Dominguez, F. (2020). SCAT Model Based on Bayesian Networks for Lost-Time Accident Prevention and Rate Reduction in Peruvian Mining Operations. In Advances in Intelligent Systems and Computing (Vol. 1209 AISC, pp. 350–358). Springer. https://doi.org/10.1007/978-3-030-50791-6_45

Register to see more suggestions

Mendeley helps you to discover research relevant for your work.

Already have an account?

Save time finding and organizing research with Mendeley

Sign up for free