基于可解释符号残差学习的水泥基材料碳化行为预测与CO2矿化潜力评估
Carbonation Behavior Prediction and CO2 Mineralization Potential Evaluation of Cement-Based Materials Based on Interpretable Symbolic Residual Learning
摘要: 水泥基材料碳化过程不仅影响混凝土耐久性，也是实现CO2矿化固定和低碳建材设计的重要途径。针对传统碳化经验模型难以同时描述材料组成、养护制度和碳化环境耦合作用，以及常规机器学习模型可解释性不足的问题，提出了一种符号回归–残差补偿学习模型(SymResNet)，用于水泥基材料碳化深度预测与CO2矿化潜力分析。首先基于公开文献和试验数据建立包含8152组样本的碳化数据库，涵盖水胶比、胶凝材料用量、矿物掺合料取代率、养护条件、CO2浓度、相对湿度、温度和碳化龄期等变量；随后利用符号回归提取碳化行为的显式表达关系，并引入XGBoost残差学习器修正复杂非线性偏差。结果表明，SymResNet模型在测试集上的R2、RMSE和MAE分别为0.883、2.043 mm和1.307 mm，明显优于单独符号回归模型，并在保持较好预测精度的同时提高了模型可解释性。SHAP分析表明，碳化环境、暴露龄期、养护制度、水胶比、胶凝材料用量、矿物掺合料取代率、相对湿度和CO2浓度是影响碳化行为的主要因素。进一步结合响应面分析和ICE-PDP分析，揭示了材料组成、孔隙连通性、湿度状态和CO2传输反应之间的耦合作用。模型指导试验结果表明，预测碳化深度与实测值吻合较好，TG和XRD结果进一步证实了碳酸盐生成。研究结果可为水泥基材料碳化行为预测、CO2矿化调控及低碳建材配合比设计提供参考。
Abstract: The carbonation process of cement-based materials not only affects the durability of concrete, but also is an important way to realize CO2 mineralization fixation and low-carbon building materials design. In order to solve the problem that it is difficult for the traditional empirical carbonation model to describe the coupling effect of material composition, curing system and carbonation environment at the same time, and the conventional machine learning model is not explanatory enough, a symbolic regression-residual compensation learning model (SymResNet) is proposed for the prediction of carbonation depth of cement-based materials and the analysis of CO2 mineralization potential. Firstly, a carbonation database containing 8152 groups of samples was established based on published literature and experimental data, covering variables such as water-binder ratio, dosage of cement-based materials, replacement rate of mineral admixtures, curing conditions, CO2 concentration, relative humidity, temperature and carbonation age. Then, the explicit expression relationship of carbonation behavior is extracted by symbolic regression, and the XGBoost residual learner is introduced to correct the complex nonlinear deviation. The results show that the R2, RMSE and MAE of SymResNet model on the test set are 0.883, 2.043 mm and 1.307 mm, respectively, which are obviously superior to the single symbolic regression model, and improve the model interpretability while maintaining good prediction accuracy. SHAP analysis shows that carbonization environment, exposure age, curing system, water-binder ratio, dosage of cementing material, replacement rate of mineral admixture, relative humidity and CO2 concentration are the main factors affecting carbonization behavior. Combined with response surface analysis and ICE-PDP analysis, the coupling effect between material composition, pore connectivity, humidity state and CO2 transport reaction was revealed. The model-guided test results show that the predicted carbonation depth is in good agreement with the measured value, and the results of TG and XRD further confirm the carbonate formation. The research results can provide a reference for the prediction of carbonation behavior of cement-based materials, the regulation of CO2 mineralization and the mix design of low-carbon building materials.
文章引用：魏恺琪. 基于可解释符号残差学习的水泥基材料碳化行为预测与CO2矿化潜力评估[J]. 土木工程, 2026, 15(10): 128-140. https://doi.org/10.12677/hjce.2026.1510245

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