The design of cementitious mixtures incorporating mine tailings as fine aggregates is a multi-objective optimization (MOO) problem, in which both the uniaxial compressive strength (UCS) and cost of the mixtures need to be considered simultaneously. Given that data-driven methods have shown promising results when solving similar MOO problems, this study developed an extreme gradient boosting regressor (XGBR) model on a dataset extracted from the literature to predict the UCS. Among the efforts taken to improve the models, a genetic algorithm (GA)-based XGBR model demonstrated the optimal prediction performance, with an R2 of 0.959. Next, the GA-XGBR model and a cost equation were used as objective functions in the MOO problem. The non-dominated sorting genetic algorithm with elite strategy (NSGA-II) was selected to solve the optimization problem. A case study was conducted, generating mixture designs that offered improved trade-offs between cost and UCS compared to experimental designs. Finally, a graphical user interface was developed to provide access to the prediction model and optimization method. Overall, this work can be used as a guide for optimal mixture designs as it facilitates informed decision-making before the actual applications.

中文翻译：

---

混合极端梯度提升回归模型，用于以细骨料形式加入矿山尾矿的水泥基混合物的多目标混合物设计优化


将矿山尾矿作为细骨料的胶凝混合物的设计是一个多目标优化 （MOO） 问题，其中需要同时考虑单轴抗压强度 （UCS） 和混合物的成本。鉴于数据驱动的方法在解决类似的 MOO 问题时显示出有希望的结果，本研究在从文献中提取的数据集上开发了一个极端梯度提升回归器 （XGBR） 模型来预测 UCS。在改进模型所做的努力中，基于遗传算法 （GA） 的 XGBR 模型展示了最佳预测性能，R2 为 0.959。接下来，将 GA-XGBR 模型和成本方程用作 MOO 问题中的目标函数。选择具有精英策略的非支配排序遗传算法 （NSGA-II） 来解决优化问题。进行了案例研究，生成了与实验设计相比，在成本和 UCS 之间提供了更好的权衡的混合物设计。最后，开发了一个图形用户界面，以提供对预测模型和优化方法的访问。总体而言，这项工作可以用作最佳混合物设计的指南，因为它有助于在实际应用之前做出明智的决策。