Investigation of carbon emission in slurry shield tunnel construction based on modified process analysis method

Underground engineering,including shield tunnel construction,is a significant contributor to carbon dioxide emissions in infrastructure engineering projects.To better predict and control the carbon emissions associated with shield tunnel construction,this paper presents a novel calculation method:the modified process analysis method based on inputoutput and process analysis methods.To evaluate the effectiveness of the proposed method,a specific shield tunnel construction project was selected as a case study.The modified process analysis method was used to analyze the various factors that influence carbon emissions during the project’s construction phase.In addition,a neural network approach was applied to validate the accuracy of the calculation using the LSTM and BP neural network.The results demonstrate that the proposed method not only combines the strengths of traditional methods but also offers high accuracy and acceptable error rates.Based on these findings,several measures to reduce carbon emissions during shield tunnel construction are suggested,providing valuable insights for reducing CO_(2) emissions associated with infrastructure engineering projects.This study highlights the importance of adopting innovative approaches to reduce carbon emissions and promotes the implementation of sustainable practices in the construction industry.Through the use of advanced analytical methods,such as the proposed modified process analysis method,we can effectively mitigate the environmental impact of construction activities and make significant contributions to the global effort to combat climate change.

地下工程中的盾构隧道施工,是基础设施工程中二氧化碳排放的重要贡献者。为了更好地预测和控制盾构隧道施工碳排放,本文提出了一种新的计算方法,即基于投入产出和过程分析方法的改进过程分析法。为评估所提方法的有效性,选取某盾构隧道施工项目作为案例,采用修正的过程分析法,对项目施工阶段影响碳排放的各种因素进行剖析。此外,采用神经网络方法,验证了LSTM和BP神经网络的计算精度。结果表明,该方法不仅结合了传统方法的优点,而且具有较高的准确率和可接受的错误率。基于这些研究结果,提出了减少盾构隧道施工过程中碳排放的若干措施,为减少与基础设施工程项目相关的CO_(2)排放提供了有价值的见解。