New improved metaheuristic approaches for optimum design of posttensioned axially symmetric cylindrical reinforced concrete walls

Abstract

SummaryTwo novel hybrid approaches are presented for optimum design of axially symmetric cylindrical walls subjected to posttensioning loads using metaheuristic algorithms such as harmony search (HS), flower pollination algorithm (FPA), and teaching learning based optimization (TLBO). The objective function of the optimization problem is to minimize the total cost of the wall subjected to constraints on the basis of sectional capacities (bending moment, shear force, and axial tension), ACI 318 (building code requirements for structural concrete) requirements and design variables such as wall thickness, compressive strength of concrete, location and intensities of posttensioning cables, size, and spacing of reinforcement. In the optimum design, the performance of the iterative population based metaheuristic algorithms, HS, FPA, and TLBO are compared and tested by taking wall thickness as discrete and continuous variable. In order to improve the efficiency on finding global optimum results, hybrid forms of the HS combined with FPA and TLBO are effective for the optimization problem.