High strength concrete (HSC) (50–100 MPa) and very-high strength concrete (VHSC) (>100 MPa) have been increasingly used in the construction industry due to its inherent performance characteristics. However, these concrete mixes have a higher carbon footprint and it is vital to consider the embodied carbon of the HSC and VHSC due to the massive consumption throughout the world. In this study, embodied carbon analysis, using machine learning algorithms has been carried out to minimize the carbon footprint of concrete without jeopardizing the mechanical properties of the concrete. Machine learning models are developed using experimental results in the literature and used to predict the compressive strength of concrete using the constituent materials. Using the experimental data and machine-learned models for mix designs, embodied carbon emissions were calculated. It is shown that there can be many mix compositions which have the same compressive strength while having significantly different embodied carbon values. Based on experimental and machine learned mix designs, an equation to predict the average embodied carbon value for concrete mixes is proposed. The study suggested proposed intervals for the benchmark function in order to propose a region where the embodied carbon value of a concrete mix should lie while achieving the desired compressive strength. Finally, it is shown that machine learning can be used successfully to identify the high strength concrete mixes while minimizing the embodied carbon value of that mix composition. Finally, guidelines are presented to produce a concrete mix within proposed benchmark limits while achieving the desirable strength grade.

高强度混凝土（HSC）（50–100 MPa）和超高强度混凝土（VHSC）（> 100 MPa）由于其固有的性能特征而越来越多地用于建筑业。但是，这些混凝土混合物的碳足迹较高，由于全世界的大量消耗，因此考虑HSC和VHSC的内含碳至关重要。在这项研究中，使用机器学习算法进行了具体的碳分析，以在不损害混凝土机械性能的情况下将混凝土的碳足迹降至最低。机器学习模型是使用文献中的实验结果开发的，并用于预测使用组成材料的混凝土的抗压强度。使用实验数据和机器学习的模型进行混合设计，计算了具体的碳排放量。结果表明，可以有许多混合组合物，它们具有相同的抗压强度，同时具有明显不同的具体碳值。基于实验和机器学习的混合料设计，提出了预测混凝土混合料平均含碳量的方程。该研究提出了基准功能的建议间隔，以便建议一个区域，在实现所需抗压强度的同时，混凝土混合物的具体碳值应位于该区域。最后，研究表明，机器学习可以成功地用于识别高强度混凝土混合物，同时使该混合物组合物的含碳量最小。最后，