TEACHING-LEARNING BASED OPTIMIZATION METHOD CONSIDERING BUCKLING AND SLENDERNESS RESTRICTION FOR SPACE TRUSSES

Abstract

The structural performance of a building is a function of several parameters and constraints whose association may offer non unique solutions which, however, meet the design requirements. Therefore, an optimization routine is needed to determine the best solution within the set of available alternatives. In this study, the TLBO method was implemented for weight-based optimization of space trusses. The algorithm applies restrictions related to the critical buckling load as well as the slenderness ratio, which are the basis to obtain reliable and realistic results. To assess the capability of the TLBO method, two reference cases and a transmission tower are subjected to the optimization analysis. In the transmission tower analysis, however, a more realistic approach is adopted as it also considers, through a safety factor, the plastic behavior in the critical buckling load constraint. With no optimization, the ideal weight increases by 101.36% when the critical buckling load is considered in the first two cases, which is consistent with the expected behavior. If the slenderness of the elements is also restricted, the ideal weight now rises by 300.78% from the original case and by 99.04% from the case where only the critical buckling load restriction is applied. Now, considering the critical buckling load and slenderness restriction with the TLBO method applied, a 18.28% reduction in the ideal weight is verified. In fact, the proposed optimization procedure converged to a better solution than that of the reference study, which is based on the genetic algorithms method.