A Single-Degree-of-Freedom system with the addition of Tuned Liquid Damper (TLD) was analyzed experimentally and numerically. In this study, a shaking table and finite element software were used in order to obtain the acceleration responses of seven structure-TLD systems. The TLD frequencies were calculated by formula and models frequencies were calculated via finite element software and experimental work. The models were excited by a sine and an earthquake load. The results obtained from the software and experimental works were compared to each other and the results have been found to be in good agreement. Finally, it was concluded that the tuned liquid damper decreases the response of a structure under earthquake excitation and sine load. In addition, it was determined that the frequency tuning ratio and mass ratio of the damper are very effective parameters in reducing of structural response.

The level of strain in structural elements is an important indicator for the presence of damage and its intensity. Considering this fact, often structural health monitoring systems employ strain gauges to measure strains in critical elements. However, because of their sensitivity to the magnetic fields, inadequate long-term durability especially in harsh environments, difficulties in installation on existing structures, and maintenance cost, installation of strain gauges is not always possible for all structural components. Therefore, a reliable method that can accurately estimate strain values in critical structural elements is necessary for damage identification. In this study, a full-scale test was conducted on a planar RC frame to investigate the capability of neural networks for predicting the strain values. Two neural networks each of which having a single hidden layer was trained to relate the measured rotations and vertical displacements of the frame to the strain values measured at different locations of the frame. Results of trained neural networks indicated that they accurately estimated the strain values both in reinforcements and concrete. In addition, the trained neural networks were capable of predicting strains for the unseen input data set.

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