This present study optimizes multiadditives for the improvement of the properties of expansive soil by integrating min‐max fuzzy goal programming model, a variant of fuzzy goal programming, into Taguchi optimization method. The multiadditives, which included ordinary Portland cement (OPC), quarry dust (QD), and sawdust ash (SDA), had various levels assigned to them in the Taguchi designed experiment, and they were all added by air‐dried weight of the expansive soil. Responses that included differential free swell, California bearing ratio, and unconfined compressive strength were determined using the Taguchi designed experiment. Concurrent analysis of the data obtained from the responses was performed using the min‐max fuzzy goal programming and the optimum combinations of additives that resulted in the significant improvement of the soils were obtained at 20% SDA, 20% QD, and 8% OPC. In addition, scanning electron micrograph analysis for the expansive soil that was combined with the optimum combination of additives showed cementitious compound formation in the soil‐additives mixtures and this confirmed lucidly that min‐max fuzzy goal programming can be integrated in Taguchi method to optimize additives for the improvement of expansive soil properties.

中文翻译：

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最小-最大模糊目标规划-Taguchi模型用于膨胀土改良中的多种添加剂优化

本研究通过将最小-最大模糊目标规划模型（一种模糊目标规划的变种）集成到Taguchi优化方法中，优化了多种添加剂以改善膨胀土的性能。在田口设计的实验中，包括普通波特兰水泥（OPC），石粉（QD）和锯末灰（SDA）在内的多种添加剂具有不同的含量，并且全部由膨胀剂的风干重量添加泥。使用Taguchi设计的实验确定包括微分自由膨胀，加利福尼亚承载比和无侧限抗压强度在内的响应。使用最小-最大模糊目标程序对从响应中获得的数据进行并发分析，并在20％SDA，20％QD和8％OPC的条件下获得了可显着改善土壤质量的最佳添加剂组合。此外，对膨胀土和最佳添加剂组合进行的扫描电子显微镜分析表明，土壤-添加剂混合物中形成胶结化合物，这很清楚地证实了可以将最小-最大模糊目标程序集成到Taguchi方法中以优化添加剂。用于改善膨胀土的特性。