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使用改进的多状态模糊贝叶斯网络进行桥梁安全风险评估的强大方法
Zhong Cao1, Weicong He1, Kaihong Chen1
1School of Electronics and Communication Engineering, Guangzhou University, Guangzhou, 510006, China.
Scientific reports
|August 20, 2025
概括
这项研究引入了多州模糊贝叶斯网络 (MFBN) 以进行可靠的桥梁安全风险评估. 该方法通过结合多个国家节点和专家可靠性来改进风险管理策略来增强传统模型.
科学领域:
- 土木工程 土木工程是指土木工程.
- 风险管理 风险管理
- 人工智能的人工智能
背景情况:
- 桥梁安全至关重要,需要强有力的风险评估方法.
- 传统的风险模型往往缺乏细粒度来捕捉复杂的故障模式.
- 评估桥梁安全需要结合专家知识和不确定性.
研究的目的:
- 为全面的桥梁安全风险评估提出一个新的多州模糊贝叶斯网络 (MFBN).
- 通过整合多状态节点分类和专家可靠性来增强现有的风险评估框架.
- 为有效的桥梁风险控制和管理策略提供基础.
主要方法:
- 建造一座桥梁崩塌断层树和一个定向非循环图来绘制因果关系.
- 将桥梁组件分为三个状态 (例如,正常,警告,故障) 而不是二进制状态.
- 开发使用专家判断,可靠性水平和信任指数的多状态模糊条件概率表.
- 应用一种改进的相似性聚合方法,包括桥梁年龄,以整合专家意见并确定根节点风险概率.
主要成果:
- 通过整合先前的知识和证据,MFBN方法有效地评估桥梁安全风险水平.
- 确定导致安全风险增加的关键节点.
- 通过对两个城市桥梁的应用,证明了有效性和稳定性.
结论:
- 拟议的MFBN方法为桥梁安全风险管理提供了有价值的决策工具.
- 与传统方法相比,该方法提供了更细致和可靠的桥梁安全评估.
- 该框架支持主动降低风险,并提高基础设施的整体安全性.
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