A novel TOPSIS linear programming model based on response surface methodology for determining optimal mixture proportions of lightweight concrete blocks containing sugarcane bagasse ash

The idea of utilizing waste from the agro-industrial sector to produce lightweight concrete is one of the good ideas for recycling and reusing waste materials. In a lightweight concrete production process, determining a material's optimal parameters is crucial, since it can help optimize the important properties of lightweight concrete blocks. This study introduces a novel TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) linear programming model, based on the Response Surface Methodology (RSM), to optimize the parameters of lightweight concrete blocks. The compressive strength, dry density, and water absorption are considered important responses, while black bagasse ash, cement with white bagasse ash, and aluminum powder are the factors considered. The proposed method successfully optimized the parameters, as confirmed by experimental results, showing a 7.22% increase in compressive strength, a 9.19% increase in dry density, and a 16.83% decrease in water absorption compared to the original condition. These improvements were achieved by using the optimal mixture ratio of 6:1:0.05 by weight, which consists of sugarcane bagasse ash, cement, and aluminum powder. The advantages of the proposed method are as follows: This paper presents a novel method called the TOPSIS linear programming model, which is a modified version of the original TOPSIS method, to calculate the closeness efficiency for each run. The proposed method is simpler and more practical, making it useful for solving multi-response optimization problems with large inputs. In addition, this research contributes to the advancement of sustainable materials and offers a practical solution for optimizing lightweight concrete block properties.