A parametric study on the cost optimization of a reinforced concrete abutment using a genetic algorithm

This paper presents the cost optimization of a reinforced concrete abutment of a cantilever type using a genetic algorithm (GA). During the optimization process, six design variables, including two geometrical design variables and four cross-sectional design variables, were considered. The objective function consists of the cost of steel, concrete, and labor. Computation programs have been developed in JAVA to find an economical design adhering to Indian Road Congress (IRC) standards. To get an optimal solution in reasonable computational time, an attempt is carried out to evaluate the optimal GA parameters for the abutment model. A parametric study was conducted to understand the effect of the angle of friction, grade of concrete, and height on the cost optimization of the abutment. From the parametric study, it is observed that optimum cost of the abutment is obtained with a higher value of the angle of friction and concrete with a lower compressive strength. The results of the optimization are further discussed in detail.