基于智能核聚变的高速公路道建设中崩风险的安全评估研究
Bo Wu1,2, Yajie Wan1, Shixiang Xu1
1School of Civil and Architectural Engineering, East China University of Technology, Nanchang, 330013, Jiangxi, China.
Heliyon
|February 26, 2024
概括
这项研究引入了一种新的多源信息融合方法,用于准确预测道崩风险. 这种方法可以通过及时调整来提高安全性,防止大规模的道塌.
科学领域:
- 地质技术工程 地质技术工程
- 风险评估 风险评估
- 数据融合数据融合
背景情况:
- 传统的道崩塌风险预测方法的准确性和及时性都很低.
- 准确的预测对于防止道项目的灾难性故障至关重要.
- 整合不同的数据源可以提高预测能力.
研究的目的:
- 开发一种先进的方法,以便及时准确地预测道塌风险.
- 通过有效的多源信息融合,提高道安全.
- 为道工程决策者提供一个强大的框架.
主要方法:
- 利用粒子优化支持向量机 (PSO-SVM) 模型来预测周围岩石的移位.
- 使用云模型 (CM) 来计算基于预测指数的基本概率赋值值.
- 应用了改进的Dempster-Shafer (D-S) 理论来融合监测数据,地质预测和现场检查信息.
主要成果:
- 拟议的多源信息融合方法在风险预测方面表现出更高的准确性.
- 申请金祖帕道项目允许及时调整风险因素.
- 综合方法成功防止了大规模的道崩.
结论:
- 多源信息融合方法,结合PSO-SVM,CM和改进的DS理论,是有效的道崩风险评估.
- 这种方法显著提高了预测的及时性和准确性.
- 该研究为提高道工程项目的安全性提供了可靠的工具.
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