info:eu-repo/semantics/article
SCAT Model Based on Bayesian Networks for Lost-Time Accident Prevention and Rate Reduction in Peruvian Mining Operations
Fecha
2020-01-01Registro en:
21945357
10.1007/978-3-030-50791-6_45
21945365
Advances in Intelligent Systems and Computing
2-s2.0-85088231547
SCOPUS_ID:85088231547
0000 0001 2196 144X
Autor
Ziegler-Barranco, Ana
Mera-Barco, Luis
Aramburu-Rojas, Vidal
Raymundo, Carlos
Mamani-Macedo, Nestor
Dominguez, Francisco
Institución
Resumen
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.