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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 |
