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Base isolators used in buildings provide both a good acceleration reduction and structural vibration control structures. The base isolators may lose their damping capacity over time due to environmental or dynamic effects. This deterioration of them requires the determination of the maintenance and repair needs and is important for the long-term isolator life. In this study, an artificial intelligence prediction model has been developed to determine the damage and maintenance-repair requirements of isolators as a result of environmental effects and dynamic factors over time. With the developed model, the required damping capacity of the isolator structure was estimated and compared with the previously placed isolator capacity, and the decrease in the damping property was tried to be determined. For this purpose, a data set was created by collecting the behavior of structures with single degrees of freedom (SDOF), different stiffness, damping ratio and natural period isolated from the foundation under far fault earthquakes. The data is divided into 5 different damping classes varying between 10% and 50%. Machine learning model was trained in damping classes with the data on the structure's response to random seismic vibrations. As a result of the isolator behavior under randomly selected earthquakes, the recorded motion and structural acceleration of the structure against any seismic vibration were examined, and the decrease in the damping capacity was estimated on a class basis. The performance loss of the isolators, which are separated according to their damping properties, has been tried to be determined, and the reductions in the amounts to be taken into account have been determined by class. In the developed prediction model, using various supervised machine learning classification algorithms, the classification algorithm providing the highest precision for the model has been decided. When the results are examined, it has been determined that the damping of the isolator structure with the machine learning method is predicted successfully at a level exceeding 96%, and it is an effective method in deciding whether there is a decrease in the damping capacity. [ABSTRACT FROM AUTHOR]

Summary: Tuned liquid dampers provide structure control with the help of the liquid mass in tanks that are attached to the structure. The mass ratio affects the optimum tuned liquid damper (TLD) parameters. This study examines the effect of mass ratio on the control performance of TLD devices in providing seismic control of structures with different damping ratios. For this purpose, TLD devices with different mass ratios were placed on two different single‐story steel and reinforced concrete structure models, and their performance under earthquake excitation was investigated. TLD parameters for obtaining the optimum displacement level in the 0.5‐ and 1.0‐s structure natural period for both structure types were optimized with the Jaya algorithm (JA), which is a metaheuristic algorithm. By using the optimum TLD parameters, the structural displacement and total acceleration values were obtained by the critical earthquake analysis. When the results are examined, it is understood that TLD mass increase from a 20% mass ratio for both structure types and selected structure periods does not have a significant effect on TLD control performance. [ABSTRACT FROM AUTHOR]

Summary: 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. [ABSTRACT FROM AUTHOR]