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使用随机森林算法调查的桥梁建设风险评估.
Ying Wu1,2, Yigang Wang3, Hongbing Liu4
1School of Media and Design, Hangzhou Dianzi University, Hangzhou, 310018, Zhejiang, China. wy43601@163.com.
Scientific reports
|September 9, 2024
概括
本研究介绍了一种机器学习方法,使用随机森林算法准确评估桥梁施工风险. 它确定了影响安全的关键因素,改进了主观的传统方法,以更好地预防事故.
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
- 机器学习应用 机器学习应用
背景情况:
- 桥梁施工安全是一个关键问题,当前的风险评估经常受到主观评估者经验的阻碍.
- 现有的方法难以确定桥梁施工事故预防的关键因素.
- 在桥梁建设中,客观,准确和高效的风险评估是至关重要的.
研究的目的:
- 开发一个客观和准确的方法来评估桥梁施工风险.
- 识别和排名在桥梁建设中最具影响力的风险因素.
- 克服桥梁安全主观基于专家的评估的局限性.
主要方法:
- 在桥梁建设中分析和分类26个人工和环境风险因素.
- 应用随机森林 (RF) 算法,一种非参数的机器学习方法.
- 使用城市综合体行人桥的案例研究验证拟议的方法.
主要成果:
- 随机森林算法提供了准确和可靠的风险评估结果,不受专家主观性的影响.
- 该研究成功地对各种风险指标的重要性进行了排名.
- 确定了影响桥梁建设风险的关键因素.
结论:
- 提出的基于随机森林的方法为客观的桥梁建设风险评估提供了可行和有效的解决方案.
- 这种方法提高了识别关键风险因素的能力,有助于改善安全管理.
- 这些发现与现实世界的风险评估是一致的,证明了其实际适用性.
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