基于云模型和Dempster Shafer证据理论的基础挖掘风险评估合并多个来源数据
Yue An1, Liuyang Li2,3, Haoyuan Gao4
1School of Investment and Construction Management, Dongbei University of Finance and Economics, Dalian, Liaoning Province, China.
Scientific reports
|March 23, 2025
概括
这项研究引入了一种新的风险评估方法,将云模型和斯特-沙弗理论整合起来,以改善挖掘前的安全分析. 该方法通过合并多源数据来提高决策准确性,从而提高准确性.
科学领域:
- 地质技术工程 地质技术工程
- 风险管理 风险管理
- 计算建模 计算建模
背景情况:
- 单一来源数据限制了挖掘前风险评估的准确性.
- 准确的可行性分析对于挖掘安全和项目成功至关重要.
研究的目的:
- 开发一个综合风险评估方法,用于开采前的可行性分析.
- 提高挖掘风险评估的准确性和全面性.
主要方法:
- 云模型 (CM) 和Dempster-Shafer (D-S) 证据理论的整合.
- 创建项目和数字数据库,使用多源风险指标.
- 通过CM成员级别和DS基本概率赋值融合多源数据.
主要成果:
- 已经实现了对挖掘风险的全面定量评估.
- 提出的方法成功评估了一个狭窄而长长的基础项目,安全风险水平为IV.
- 证明了多来源风险评估指标的有效融合.
结论:
- 综合的CM-D-S方法显著提高了开采前风险评估的准确性.
- 为基础挖掘的预防策略和管理决策提供科学基础.
- 解决了可行性研究中单一来源数据的局限性.
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