Eight percent of global carbon dioxide emissions can be attributed to the production of cement, the main component of concrete, which is also the dominant source of CO2 emissions in the construction of data centers. The discovery of lower-carbon concrete formulae is therefore of high significance for sustainability. However, experimenting with new concrete formulae is time consuming and labor intensive, as one usually has to wait to record the concrete's 28-day compressive strength, a quantity whose measurement can by its definition not be accelerated. This provides an opportunity for experimental design methodology like Bayesian Optimization (BO) to accelerate the search for strong and sustainable concrete formulae. Herein, we 1) propose modeling steps that make concrete strength amenable to be predicted accurately by a Gaussian process model with relatively few measurements, 2) formulate the search for sustainable concrete as a multi-objective optimization problem, and 3) leverage the proposed model to carry out multi-objective BO with real-world strength measurements of the algorithmically proposed mixes. Our experimental results show improved trade-offs between the mixtures' global warming potential (GWP) and their associated compressive strengths, compared to mixes based on current industry practices.

翻译：全球二氧化碳排放量的8%可归因于水泥（混凝土的主要成分）的生产，而水泥也是数据中心建设过程中二氧化碳排放的主要来源。因此，发现低碳混凝土配方对可持续发展具有重要意义。然而，试验新型混凝土配方既耗时又费力，因为研究人员通常需等待记录混凝土的28天抗压强度——这一指标的测量按其定义无法加速。这为贝叶斯优化等实验设计方法加速寻找高强度且可持续的混凝土配方提供了契机。本文中，我们：1) 提出建模步骤，使得通过高斯过程模型基于相对较少的测量数据即可准确预测混凝土强度；2) 将可持续混凝土的搜索问题转化为多目标优化问题；3) 利用所提出的模型，结合算法建议配方的实际强度测量数据，开展多目标贝叶斯优化。实验结果表明，与基于当前行业实践的配方相比，我们的方法在混合物的全球变暖潜能值（GWP）与其对应的抗压强度之间实现了更优的权衡。