A comprehensive risk assessment method for hot work in underground mines based on G1-EWM and unascertained measure theory

加权 工作(物理) 度量(数据仓库) 风险评估 计算机科学 数据挖掘 可靠性工程 风险分析(工程) 运筹学 工程类 机械工程 计算机安全 医学 放射科
作者
Xiaoqiang Ding,Xiangliang Tian,Jinhui Wang
出处
期刊:Scientific Reports [Nature Portfolio]
卷期号:14 (1) 被引量:6
标识
DOI:10.1038/s41598-024-56230-y
摘要

Abstract A risk assessment method for hot work based on G1-EWM and unascertained measurement theory was proposed to prevent hot work accidents in underground mines. Firstly, based on the risk influencing factors and classification criteria for underground hot work operations in mines, a single indicator measurement matrix was constructed using unascertained measurement theory; Secondly, a risk assessment index system for mine underground hot work operations was established. The combination weight coefficient of each index was determined using the order relationship analysis method (G1) and entropy weight method (EWM) and coupled with the single index measurement evaluation vector to calculate the multi-index comprehensive evaluation vector of the evaluation object; Finally, the model was validated and examined using engineering examples, and the evaluation level was determined using confidence identification criteria. The results showed that the proposed method, when used to evaluate the risk of hot work operations in tunnels and vertical shafts in metal mines, produces risk levels that are in line with reality III (Moderate Risk) for the vertical shaft and IV (High Risk) for the tunnels. The evaluation model results are consistent with the risk evaluation results the whole process of on-site hot work, which verifies the model feasibility. A unique strategy and method for risk management in hot work operations in underground mines is provided by the combination of weighting and unascertained measure models, which has theoretical and practical value. Future research could focus on refineing this model by exploring the applicability in diverse mining environments and integrating advanced analytical techniques to enhance the predictive accuracy and operational efficiency.

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