Key parameters of soil corrosivity and a model for predicting the corrosion rate of Q235 steel in Beijing

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.

Keywords

• beijing soil
• q235 steel
• corrosion rate
• key parameters
• machine learning
• corrosion prediction