Optimum Design of Reinforced Concrete Beams using Teaching- Learning-Based Optimization

Efficiency of reinforced concrete (RC) design can be measured by finding the design with minimum cost within all possible ones that satisfied security conditions. This task is difficult because RC members consisting of two materials; concrete and steel bars with different material properties i.e., compressive and tensile strength, elasticity modulus, and unit material costs. Thus, an additional process must be used to obtain optimum design of RC member. Metaheuristic algorithms that has been successfully applied to various optimization problems can be a suitable solution. In this paper, teaching learning based optimization (TLBO) method, one of the recently developed metaheuristic algorithm, is applied for optimum design of RC beams. In order to show efficiency of the TLBO algorithm, results were also compared with harmony search (HS) and bat algorithm (BA). RC design is done according to ACI 318-05 (Building code requirements for structural concrete) code and several cases with different flexural moments were optimized to test the approaches. According to analyses results, TLBO approach is a competitive method. Key-Words: Reinforced concrete beams, Optimization, Teaching learning based optimization, Bat algorithm, Harmony search, Metaheuristic algorithms.