Key parameters of soil corrosivity and a model for predicting the corrosion rate of Q235 steel in Beijing
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摘要: 在北京101處不同地理位置進行了土壤現場取樣,并在實驗室對土壤樣品的9項理化參數進行了測試,獲得了北京地區的土壤參數分布范圍. 通過機器學習分析得到了Q235鋼在北京地區土壤中腐蝕速率的關鍵影響因素為自腐蝕電位、土壤含水率以及土壤電阻率,基于隨機森林算法建立了Q235鋼在北京土壤中的腐蝕速率預測模型,預測值與實際值的平均絕對誤差小于5%. 為了進一步探究Q235鋼在北京土壤中的腐蝕速率與3項關鍵土壤參數之間的關系,利用已建立的腐蝕速率預測模型,以自腐蝕電位、土壤電阻率以及土壤含水率3項關鍵參數為輸入量,以Q235鋼腐蝕速率為輸出量進行預測分析,預測結果表明:當自腐蝕電位在?0.57 V(vs SCE)~?0.70 V(vs SCE)之間、含水率在13%~22%以及土壤電阻率在45 Ω·m~65 Ω·m之間時,碳鋼在土壤中的腐蝕速率較高,超過了0.1 mm·a?1,該結果為低碳鋼在北京地區土壤中的腐蝕評估提供了相對簡單的方法.Abstract: 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 key soil parameters, the established prediction model was employed. Taking the soil self-corrosion potential, resistivity, and moisture content as the input, the Q235 steel corrosion rate was predicted as the output and was analyzed. The prediction results show that when the soil self-corrosion potential is between ?0.57 V(vs SCE) and ?0.70 V(vs SCE), the soil moisture content is between 13% and 22% and when the soil resistivity is between 45 and 65 Ω·m, the corrosion rate of carbon steel in the soil is higher than 0.1 mm·a?1. This work provides a simple method for assessing the corrosion of low-carbon steel in Beijing.
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Key words:
- Beijing soil /
- Q235 steel /
- corrosion rate /
- key parameters /
- machine learning /
- corrosion prediction
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圖 7 土壤各理化參數的均方根誤差
(0—土壤電阻率;1—氧化還原電位;2—土壤含水率;3—pH;4—自腐蝕電位;5—土壤含鹽量;6—氯離子含量)
Figure 7. Root mean square errors (RMSE) of soil physical and chemical parameters
(0—soil resistivity; 1—redox potential; 2—soil moisture content; 3—pH; 4—soil self-corrosion potential; 5—soil salt content; 6—chloride content)
表 1 Q235鋼成分表(質量分數)
Table 1. Q235–steel composition contents
% C Si Mn P S Al Fe 0.21 0.043 0.29 0.022 0.009 0.025 Bal 表 2 北京市土壤各理化參數的范圍
Table 2. Ranges of physical and chemical parameters of the Beijing soil
Soil texture pH Soil moisture content/% Redox potential/ mV(vs SCE) Soil resistivity/ (Ω·m) Soil self-corrosion potential/V(vs SCE) Chloride-ion content/% Soil salt content/% Soil self-corrosion current density/ (A·m?2) Loam soil 6.9–9.0 5–27 37.6–357.8 13.2–131.5 (?0.4)–(?0.79) 4.1 × 10?4–
3.94 × 10?30.03–0.245 1.72 × 10?4–9.95 × 10?2 表 3 預測誤差分析統計
Table 3. Prediction-error analysis statistics
RMSE MAE R2 0.0134 0.00026 0.9048 259luxu-164 -
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