工程施工碳排放不确定性建模与方案鲁棒性优化研究

Research on Establishing Carbon Emission Models with Uncertainty and Optimizing the Robustness of Schemes During Engineering Construction

工程施工阶段碳排放密集,实践情景有众多的不确定性因素影响碳排放计量和低碳施工方案制定。为实现复杂不确定性因素影响下施工阶段的碳排放计量,提供不确定性背景下的决策支持,基于离散事件仿真、碳排放计量、机器学习、鲁棒性优化等理论,构建了施工碳排放不确定性建模和施工方案碳排放鲁棒性优化模型。为验证方法的有效性,以实际工程施工项目为例,对碳排放进行不确定性计量,并以此为基础优化低碳施工方案。结果表明,构建的方法能够优化施工方案碳排放鲁棒性,在不确定性背景下使施工过程稳定保持低碳排放水平,以此作为指导低碳工程施工的重要理论依据。

The carbon emissions generated during the construction phase are considerable,and the real-world scenario is replete with uncertainty,which can influence the measurement of carbon emissions and the formulation of a low-carbon construction plan.Considering the influence of uncertain factors,this paper proposes a method for uncertain measurement of construction carbon emission based on discrete event simulation,carbon emission measurement,machine learning,and robustness optimization.The objective is to provide decision-makers with decision support by carrying out construction carbon emission measurement and offering insights into the potential impact of uncertain factors on the measurement process.A method for optimizing the robustness of carbon emissions in construction schemes is also established.To verify the effectiveness of the method,a model for uncertain measurement of carbon emissions is constructed using a practical construction project as an example,and the low-carbon construction scheme is optimized on this basis.The results demonstrate that this method can enhance the resilience of carbon emissions associated with construction projects,maintaining a low-carbon emission profile in the face of uncertainty.This method serves as a significant theoretical reference for guiding the construction of low-carbon projects.