A metaheuristic-based optimum tuning approach for tuned liquid dampers for structures

This study focuses on the optimization of tuned liquid dampers (TLDs) by using metaheuristic methods using a natural phenomenon as inspiration. Unlike tuned mass dampers (TMDs), TLDs consist of two parts of bodies that contain the passive liquid that is stationary and the active part of the liquid that involves in sloshing. To optimize the amount of optimum active and passive liquids by finding the best dimensional properties of the liquid tank, a two-degree-of-freedom (DOF) model was used as TLD in the dynamic analysis used in the optimization that aims to reduce the maximum displacement of the structure under earthquake excitations. For this purpose, water, oil, and glycerin liquids were selected for TLD. TLD parameters were optimized with the help of Jaya algorithm, flower pollination algorithm (FPA), harmony search (HS) algorithm, and teaching-learning-based optimization (TLBO). Total acceleration and maximum displacement were evaluated after optimization. The performance of TLD under earthquake excitations has been also compared with TMDs. According to the results, the optimized TLD has a better performance than TMD, and Jaya algorithm has a slightly better performance than the other algorithms.