基于机器学习的腐蚀率预测嵌入在土壤中的钢
Zheng Dong1,2,3, Ling Ding4, Zhou Meng1
1College of Civil Engineering, Zhejiang University of Technology, Hangzhou, China.
Scientific reports
|August 6, 2024
概括
机器学习准确地预测了埋在土壤中的钢铁腐蚀. 随机森林模型使用电阻,曝光时间和有机碳,为基础设施寿命提供最佳的腐蚀电流密度预测.
科学领域:
- 材料科学 材料科学 材料科学
- 腐蚀工程 腐蚀工程
- 计算科学 计算科学
背景情况:
- 预测埋在土壤中的钢铁腐蚀对于基础设施使用寿命评估至关重要.
- 由于复杂的土壤环境变量,现有的模型缺乏准确性.
- 机器学习为提高腐蚀预测提供了一个有希望的方法.
研究的目的:
- 开发和比较机器学习算法,用于预测土壤埋藏钢的腐蚀电流密度.
- 确定影响土壤中钢铁腐蚀的关键环境因素.
- 优化一个预测模型,用于准确的腐蚀评估.
主要方法:
- 采用了三种机器学习算法:随机森林,支持向量回归和多层感知.
- 利用了来自威斯康星州的实验室测试的土壤样本,测量了水分,pH,电阻,化物,硫酸盐和有机碳等变量.
- 通过极化技术测量了钢铁腐蚀电流密度作为模型输出.
主要成果:
- 随机森林 (RF) 模型实现了最高的可预测性,RMSE为0.01095 A/m2和R2为0.987.
- 电阻被确定为腐蚀的最重要的预测因素.
- 电阻,暴露时间和平均总有机碳的组合提供了最佳的预测准确性.
结论:
- 机器学习,特别是随机森林算法,显著提高了土壤埋藏钢铁腐蚀的预测准确度.
- 电阻是影响土壤环境中腐蚀速率的关键因素.
- 使用关键特征的优化模型提高了对结构完整性和使用寿命的评估.
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