Self-compacting concrete (SCC) is a liquid mixture appropriate for putting in structures with excessive reinforcement without vibration. The application of SCC has found wide use in practice. However, its application is often limited by lack of knowledge on mix material gained from laboratory tests. This paper presents a nonparametric mathematical method for the design of SCC mixes containing fly ash, which called as data envelopment analysis (DEA). DEA have the ability to estimate a set of units (a unit is consisted of multi-input–multi-output), in order to determine their efficiencies. To create DEA models, a database of experimental data was collected from the technical literature and applied. The data applied in the data envelopment analysis approach are organized in a format of six inputs parameters that contain superplasticizer, coarse aggregates, fine aggregates, water–binder ratio, fly ash replacement percentage, and the total binder content. Four outputs parameters are predicted based on the DEA method as the V-funnel time, the slump flow, the L-box ratio, and the cylindrical compressive strength at 28 days of SCC including fly ash. In this paper, we predict the optimal level of input required to produce the level of output required by SCC using DEA. To validate the usefulness of the suggested model and better its proficiency, a comparison of the DEA model with other investigator’s empirical results and other models results such as ANN was performed, and a good assent was gained.

自密实混凝土（SCC）是一种液体混合物，适合放置在没有振动的情况下过度加固的结构。在实践中，SCC的应用得到了广泛的应用。然而，由于缺乏对从实验室测试中获得的混合材料的知识，其应用常常受到限制。本文提出了一种用于设计含粉煤灰的SCC混合料的非参数数学方法，称为数据包络分析（DEA）。DEA可以估算一组单位（一个单位由多输入多输出组成），以便确定其效率。为了创建DEA模型，从技术文献中收集并应用了实验数据的数据库。数据包络分析方法中应用的数据以包含超级增塑剂的六个输入参数的格式进行组织，粗骨料，细骨料，水灰比，粉煤灰替代率和总粘合剂含量。根据DEA方法预测了四个输出参数，即V漏斗时间，坍落度流量，L箱比和SCC（包括粉煤灰）在28天时的圆柱抗压强度。在本文中，我们预测使用DEA产生SCC所需的输出水平所需的最佳输入水平。为了验证建议模型的有效性和更好的熟练度，将DEA模型与其他研究者的经验结果和其他模型结果（例如ANN）进行了比较，并获得了良好的认可。