Abstract: Railway tunnel lining construction is a crucial part of tunnel construction, and its carbon emissions cannot be ignored. In order to solve the problems of insufficient accuracy and poor generalization ability of carbon emission prediction results caused by unclear key carbon emission sources and influencing factors of railway tunnel lining construction, a screening method and its prediction model of carbon emission influencing factors of railway tunnel lining construction based on feature engineering and XGBoost were proposed. Firstly, the calculation boundary of the railway tunnel lining construction stage was defined, and the carbon emission calculation model of modular lining construction based on process unit was constructed. Secondly, the out-of-bag (OOB) estimation and mutual information algorithms in the random forest were used to remove redundancy from the initial feature set, and the OOB error rate was used as the evaluation index to screen out the optimal influencing factor set. Finally, the extreme gradient boosting (XGBoost) algorithm was used to predict carbon emissions, and the partial dependence plot (PDP) was introduced to reveal the marginal influence effect between feature variables and carbon emissions. Taking a railway tunnel in southwest China as a case study for verification, the results show that in the case tunnel, the carbon emissions from shotcrete, steel frames, connecting steel bars and bolt support account for the highest proportion, totaling over 70%, and in the consumption of energy materials, concrete and steel contribute the most to carbon emissions, accounting for over 80% in total. The feature engineering-XGBoost model was verified. The numerical values of various evaluation indicators showed that the proposed model had good results, the optimal subset C={surrounding rock grade, construction method, buried depth, steel frame type, reserved deformation} was finally determined, which visually analyzed the influence mechanism of different features.

Key words: tunnel engineering; tunnel lining; feature engineering; influencing factors; carbon emission prediction; extreme gradient boosting algorithm

摘要： 铁路隧道衬砌施工作为隧道施工的关键环节，其碳排放量不可忽视。为解决因铁路隧道衬砌施工关键碳排放源及影响因素不清晰导致的碳排放预测结果不准确、泛化能力较差的问题，提出了基于特征工程与极限梯度提升算法(XGBoost)的铁路隧道衬砌施工碳排放影响因素筛选方法及其预测模型。首先，界定铁路隧道衬砌施工阶段的计算边界，构建基于工序单元的模块化衬砌施工碳排放计算模型；其次，运用随机森林中的袋外估计和互信息两种算法，对初始特征集进行去冗余，以袋外误差(OOB)错误率为评价指标筛选出最优影响因素集；最后，运用XGBoost进行碳排放预测，并引入部分依赖图（PDP）揭示特征变量与碳排放量之间的边际影响效应。以西南某铁路隧道为案例进行验算，结果显示：在案例隧道中，喷射混凝土、钢架与连接钢筋、锚杆支护的碳排放占比最高，合计超过70%；在能源材料消耗中，混凝土和钢材产生的碳排放最多，合计超过80%；对特征工程-XGBoost模型进行验证，各项评估指标的数值表明模型具有良好的效果，最终确定最优子集C={围岩等级、施工工法、埋深、钢架类型、预留变形量}为最优影响因素集，并可视化分析了不同特征的影响机理。

关键词: 隧道工程；隧道衬砌；特征工程；影响因素；碳排放预测；极限梯度提升算法