Min‐max fuzzy goal programming ‐ Taguchi model for multiple additives optimization in expansive soil improvement

Abstract

AbstractThis 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.