Development of robust concrete mixes with a lower environmental impact is challenging due to natural variability in constituent materials and a multitude of possible combinations of mix proportions. Making reliable property predictions with machine learning can facilitate performance-based specification of concrete, reducing material inefficiencies and improving the sustainability of concrete construction. In this work, we develop a machine learning algorithm that can utilize intermediate target variables and their associated noise to predict the final target variable. We apply the methodology to specify a concrete mix that has high resistance to carbonation, and another concrete mix that has low environmental impact. Both mixes also fulfill targets on the strength, density, and cost. The specified mixes are experimentally validated against their predictions. Our generic methodology enables the exploitation of noise in machine learning, which has a broad range of applications in structural engineering and beyond.

翻译：由于组成材料的自然变异性和大量可能的配合比组合，开发具有较低环境影响的稳健混凝土配合比具有挑战性。利用机器学习进行可靠的性能预测，有助于实现基于性能的混凝土规范，减少材料使用效率低下问题，并提升混凝土结构施工的可持续性。本研究开发了一种机器学习算法，该算法能够利用中间目标变量及其相关噪声来预测最终目标变量。我们将该方法应用于规范两种混凝土配合比：一种具有高抗碳化性能，另一种具有较低环境影响。这两种配合比均满足强度、密度和成本方面的目标值。通过实验验证了所设计配合比与预测结果的一致性。本文提出的通用方法能够利用机器学习中的噪声，在结构工程及更广泛领域具有广阔的应用前景。