实验性表征和机器学习模型的泄漏诱导的水分分配系统的土壤流化
Masoud Ghodsian1, Shima Mohammadbeigi1
1Faculty of Civil and Environmental Engineering, Tarbiat Modares University, Tehran, Iran.
PloS one
|September 23, 2025
概括
水管道泄漏导致土壤流化,导致水坑和基础设施损坏. 这项研究开发了预测模型和机器学习方法,以有效地理解和减轻这些风险.
科学领域:
- 地质技术工程 地质技术工程
- 流体动力学 流体动力学
- 民用基础设施 民用基础设施
背景情况:
- 水分系统的泄漏是一个重大的全球问题,导致资源损失,土壤侵蚀和沉坑形成,威胁基础设施的稳定性.
- 了解土壤流化过程,即压力泄漏会产生乱的水土混合物,对于防止灾难性的故障至关重要.
研究的目的:
- 研究由管道泄漏引起的土壤流化机制和动态.
- 开发流体化区域尺寸的预测模型,并评估关键因素的影响.
- 评估整体机器学习模型的性能,以提高预测准确性和稳定性.
主要方法:
- 使用定制泄漏模拟装置进行实验测试.
- 应用维度分析以确定关键影响因素并开发经验方程.
- 利用集体机器学习模型 (随机森林,XGBoost,堆叠) 进行预测和验证.
主要成果:
- 开发了经验方程,预测流体化区域的高度和面积,R2值高达0.915.5.
- 合并模型显示出不同的性能:XGBoost提供了最高的准确性,随机森林提供了最佳的概括,堆叠模型平衡了两者.
- 确定密度对称的弗劳德数和土壤均性作为影响流化的主要因素,对模型预测有重大影响.
结论:
- 经验和机器学习模型提供了可靠的工具来预测土壤流化程度和评估沉坑风险.
- 调查结果为基础设施脆弱性评估和风险减轻策略提供了关键的见解.
- 时间分析揭示了流体化的不同阶段,有助于理解泄漏引起的土壤不稳定性的进展.
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