Research on carbon emission prediction and influencing factors in the embodied stage of railway track engineering

被引：0
|
作者：
Bao, Xueying [1 ]
Han, Tong [1 ]
Huo, Yuyu [1 ]
机构：
[1] College of Civil Engineering, Lanzhou Jiaotong University, Lanzhou,730070, China
来源：
Journal of Railway Science and Engineering | 2024年 / 21卷 / 10期
关键词：
Under the national double carbon strategic goal; the low-carbon transformation of the railway field is imperative. As an important part of railway engineering; the carbon emission generated in the embodied stage is an important source of carbon emission in railway engineering. In order to quantify carbon emissions in the embodied stage of railway track engineering and realize intelligent analysis; this paper established a calculation model of carbon emissions in the embodied stage of railway track engineering; and it also proposed a model for predicting carbon emission and analyzing influencing factors based on machine learning algorithm. First; the research boundary of embodied stage was defined; the railway track engineering was decomposed; and the carbon emission factor method was used to establish the carbon emission calculation model with the main process as the basic accounting unit. Second; the Light Gradient Boosting Machine (LightGBM) was used to build a carbon emission prediction model. An interpretable machine learning model (SHAP) was introduced to analyze the contribution of influencing factors to carbon emissions. By taking a railway track project in a southwestern mountainous area as an example; a typical unit rail section was selected to calculate carbon emissions. The results shows that the total carbon emission of 1 km length is 1 290.94 t; and the carbon emissions of material production stage accounted for the largest proportion of about 87.21%. The carbon emissions of track-laying and roadbed are relatively high; which are 47.44% and 46.44%; respectively. The LIGTBM-SHAP model was verified by extracting relevant characteristics of carbon emissions from the track engineering as influencing factors. The numerical value of each evaluation index shows that the model has a good prediction effect; and the importance rank of influencing factors in descending order are track structure form; line section; sleeper type or track plate; construction days; section slope; and section transportation distance. In the analysis of results; the single factor feature dependence graph was used to clarify the impact of categorical variables or numerical changes of each influencing factor on carbon emissions. The research results provide a more intelligent and comprehensive research model for carbon emission calculation; prediction; and analysis of railway track engineering; and they also provide a reference for carbon emission reduction work in railway construction. © 2024; Central South University Press. All rights reserved;
D O I：
10.19713/j.cnki.43-1423/u.T20240050
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页码：4299 / 4310
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