摘要
在北京101处不同地理位置进行了土壤现场取样,并在实验室对土壤样品的9项理化参数进行了测试,获得了北京地区的土壤参数分布范围.通过机器学习分析得到了Q235钢在北京地区土壤中腐蚀速率的关键影响因素为自腐蚀电位、土壤含水率以及土壤电阻率,基于随机森林算法建立了Q235钢在北京土壤中的腐蚀速率预测模型,预测值与实际值的平均绝对误差小于5%.为了进一步探究Q235钢在北京土壤中的腐蚀速率与3项关键土壤参数之间的关系,利用已建立的腐蚀速率预测模型,以自腐蚀电位、土壤电阻率以及土壤含水率3项关键参数为输入量,以Q235钢腐蚀速率为输出量进行预测分析,预测结果表明:当自腐蚀电位在-0.57 V(vs SCE)~-0.70 V(vs SCE)之间、含水率在13%~22%以及土壤电阻率在45Ω·m~65Ω·m之间时,碳钢在土壤中的腐蚀速率较高,超过了0.1 mm·a^(-1),该结果为低碳钢在北京地区土壤中的腐蚀评估提供了相对简单的方法.
Soil samples were excavated from 101 geographical locations in Beijing and transported back to a laboratory.The samples were tested for nine physical and chemical parameters,and the distribution ranges of the soil parameters were obtained.The soil in Beijing is mainly loam,involving clay and sand,with the pH being mainly neutral or weakly alkaline;its chloride content is low.Additionally,the soil parameters that vary substantially are the moisture content,resistivity,self-corrosion potential,redox potential,and self-corrosion current density.Herein,because of the long period required,in addition to the difficulty of burying corrosion-inspection pieces in the field,weight-loss experiments were performed in seven locations.Moreover,the corrosion rates calculated using Faraday’s law and the weight-loss method were compared and verified for seven locations.The results revealed that the corrosion rate obtained using Faraday’s law is consistent with that obtained using the weight-loss method.Therefore,the corrosion-rate data obtained using Faraday’s law in the laboratory have a certain practical significance;such data can provide support for follow-up research and analysis.The characteristics of the soil parameters and the correlation among different such parameters were obtained using the machine learning random-forest algorithm and Pearson coefficient analysis.The results reveal the soil self-corrosion potential,water content,and resistivity to be the key factors affecting the Q235 steel corrosion rate for the Beijing soil.The corrosion–rate prediction model of Q235 steel for the Beijing soil was established based on the machine learning random-forest algorithm.An average absolute error of<5%(which is small)was found between the predicted and actual values of the corrosion rate.The prediction model can,therefore,better reflect the soil corrosivity in Beijing,which has a certain practical significance.To further explore the relationship between the Q235 steel corrosion rate for the Beijing soil and the three k
作者
尹志彪
王莎莎
祝振洪
谷少杰
马帅杰
杜艳霞
江社明
YIN Zhibiao;WANG Shasha;ZHU Zhenhong;GU Shaojie;MA Shuaijie;DU Yanxia;JIANG Sheming(Central Iron&Steel Research Institute,Beijing 100081,China;Beijing Gas Group Company Limited,Beijing 100035,China;Institute for Advanced Materials and Technology,University of Science and Technology Beijing,Beijing 100083,China)
出处
《工程科学学报》
EI
CSCD
北大核心
2023年第11期1939-1947,共9页
Chinese Journal of Engineering
关键词
北京土壤
Q235钢
腐蚀速率
关键参量
机器学习
腐蚀预测
Beijing soil
Q235 steel
corrosion rate
key parameters
machine learning
corrosion prediction
