为探究不同因素对混凝土性能的影响，采用响应面法来优化混凝土配合比，试验以水胶比、钢渣掺量、沙漠砂掺量为变量，重点分析了混凝土坍落度、抗压强度和劈裂抗拉强度等主要性能指标。试验结果显示，沙漠砂掺量对混凝土坍落度影响最显著，抗压强度与劈裂抗拉强度主要受水胶比变化的影响。随着沙漠砂掺量的增加，混凝土坍落度、抗压强度、劈裂抗拉强度呈现先增加后减小的趋势，在沙漠砂掺量在30%时，达到其性能最优。钢渣的加入可以改善沙漠砂混凝土（desert sand concrete, DSC）的流动性，随着钢渣的增加，DSC的抗压强度呈现减小趋势，抗拉强度先增加后减小；钢渣的加入与沙漠砂有交互作用，尤其是对DSC的抗拉强度。通过响应分析，得到最优配合比为水胶比0.39、钢渣掺量10%、沙漠砂掺量为30%，此时DSC综合性能最优。最后，使用非支配排序遗传算法Ⅱ（non-dominated sorting genetic algorithm Ⅱ，NSGA-Ⅱ）进行多目标优化，该优化得到更加完善的解集，从而为DSC的应用提供一定的技术支撑。

To explore the impacts of various factors on the performance of concrete, the response surface methodology was adopted to optimize the concrete mix proportion. In the experiment, the water-binder ratio, the dosage of steel slag, and the content of desert sand were taken as variables, with a focus on analyzing the main performance indicators such as the slump of concrete, compressive strength, and splitting tensile strength. The experimental results indicate that the content of desert sand has the most significant influence on the slump of concrete, while the compressive strength and splitting tensile strength are mainly affected by the variation of the water-binder ratio. With the increase of the content of desert sand, the slump, compressive strength, and splitting tensile strength of concrete exhibit a trend of initially increasing and then decreasing. When the content of desert sand reaches 30%, the performance is optimal. The addition of steel slag can enhance the fluidity of desert sand concrete (DSC). As the amount of steel slag increases, the compressive strength of DSC shows a decreasing trend, and the tensile strength increases initially and then decreases. The addition of steel slag interacts with desert sand, particularly on the tensile strength of DSC. Through response analysis, the optimal mix proportion was obtained as a water-binder ratio of 0.39, a dosage of steel slag of 10%, and a content of desert sand of 30%, at which the comprehensive performance of DSC is the best. Finally, non-dominated sorting genetic algorithm Ⅱ（NSGA-Ⅱ) was utilized for multi-objective optimization, which yielded a more complete solution set, thereby providing certain technical support for the application of DSC.