A comprehensive review of the statistical experimental optimization problem concerning the mixture design of various cement-based materials is presented herein. This review summarizes and discusses over 80 applications of optimum design regarding the basic test information under response surface method (RSM), including influence factor and corresponding response, statistical method, and coefficient of determination. The statistical experimental design reported in previous studies has shown that RSM is a sequential procedure to provide a suitable approximation for the mixture optimization. Then, linear, quadratic and interactive relationships of the statistical model can be evaluated available. Especially, the multi-objective optimization issues with multiple or competing performance requirements for various cement-based materials have also been reported, by considering fluidity, strength development, environmental impact, cost and durability. Overall, the results from existing publications have demonstrated that statistical inference and analysis of variance (ANOVA) are suitable for mix proportion design and process optimization of cement-based materials. The W/B ratio and mixture components are the prevalent factors in experimental design optimization, and then the fluidity and strength as the most popularly used response. Thus, theoretical optimum mixture proportioning can be used to predict valuable fresh and hardened properties. Finally, a critical discussion of the selection of design strategy, independent factors and their responses, and the experimental region involved in statistical experimental design, is provided. Based on this review, we conclude that the multi-objective optimization approaches need a further systematic study, and further studies of sustainable concrete optimization are needed by comparing the different chemical composition and particle characteristics.

本文介绍了有关各种水泥基材料的混合物设计的统计实验优化问题的全面综述。这篇综述总结和讨论了关于响应面法（RSM）下基本测试信息的优化设计的80多种应用，包括影响因素和相应的响应，统计方法以及确定系数。先前研究中报道的统计实验设计表明，RSM是一种顺序过程，可为混合物优化提供合适的近似值。然后，可以评估统计模型的线性，二次和交互关系。特别，通过考虑流动性，强度发展，环境影响，成本和耐用性，还报道了对多种水泥基材料具有多重或竞争性能要求的多目标优化问题。总体而言，现有出版物的结果表明，统计推断和方差分析（ANOVA）适用于水泥基材料的配合比设计和工艺优化。W / B比和混合物组分是实验设计优化中的主要因素，然后流动性和强度是最常用的响应。因此，理论上最佳的混合物配比可用于预测有价值的新鲜和硬化性能。最后，对设计策略的选择，独立因素及其响应进行了严格的讨论，并提供了涉及统计实验设计的实验区域。在此基础上，我们得出结论，多目标优化方法需要进一步的系统研究，并且需要通过比较不同的化学成分和颗粒特性来进一步研究可持续混凝土优化。