• Journal of Korean Association for Spatial Structures
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• v.17 no.4
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• pp.123-132
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• 2017

A smart tuned mass damper (TMD) was developed to provide better control performance than a passive TMD for reduction of earthquake induced-responses. Because a passive TMD was developed decades ago, optimal design methods for structural parameters of a TMD, such as damping constant and stiffness, have been developed already. However, studies of optimal design method for structural parameters of a smart TMD were little performed to date. Therefore, parameter studies of structural properties of a smart TMD were conducted in this paper to develop optimal design method of a smart TMD under seismic excitation. A retractable-roof spatial structure was used as an example structure. Because dynamic characteristics of a retractable-roof spatial structure is changed based on opened or closed roof condition, control performance of smart TMD under off-tuning was investigated. Because mass ratio of TMD and smart TMD mainly affect control performance, variation of control performance due to mass ratio was investigated. Parameter studies of structural properties of a smart TMD was performed to find optimal damping constant and stiffness and it was compared with the results of optimal passive TMD design method. The design process developed in this study is expected to be used for preliminary design of a smart TMD for a retractable-roof spatial structure.

• Journal of Korean Association for Spatial Structures
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• v.17 no.3
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• pp.107-115
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• 2017

In this paper, a structural design method of a smart tuned mass damper (TMD) for a retractable-roof spatial structure under earthquake excitation was proposed. For this purpose, a retractable-roof spatial structure was simplified to a single degree of freedom (SDOF) model. Dynamic characteristics of a retractable-roof spatial structure is changed based on opened or closed roof condition. This condition was considered in the numerical simulation. A magnetorheological (MR) damper was used to compose a smart TMD and a displacement based ground-hook control algorithm was used to control the smart TMD. The control effectiveness of a smart TMD under harmonic and earthquake excitation were evaluated in comparison with a conventional passive TMD. The vibration control robustness of a smart TMD and a passive TMD were compared along with the variation of natural period of a simplified structure. Dynamic responses of a smart TMD and passive TMD under resonant harmonic excitation and earthquake load were compared by varying mass ratio of TMD to total mass of the simplified structure. The design procedure proposed in this study is expected to be used for preliminary design of a smart TMD for a retractable-roof spatial structure.

• Journal of Korean Association for Spatial Structures
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• v.21 no.2
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• pp.41-48
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• 2021

A smart tuned mass damper (TMD) is widely studied for seismic response reduction of various structures. Control algorithm is the most important factor for control performance of a smart TMD. This study used a Deep Deterministic Policy Gradient (DDPG) among reinforcement learning techniques to develop a control algorithm for a smart TMD. A magnetorheological (MR) damper was used to make the smart TMD. A single mass model with the smart TMD was employed to make a reinforcement learning environment. Time history analysis simulations of the example structure subject to artificial seismic load were performed in the reinforcement learning process. Critic of policy network and actor of value network for DDPG agent were constructed. The action of DDPG agent was selected as the command voltage sent to the MR damper. Reward for the DDPG action was calculated by using displacement and velocity responses of the main mass. Groundhook control algorithm was used as a comparative control algorithm. After 10,000 episode training of the DDPG agent model with proper hyper-parameters, the semi-active control algorithm for control of seismic responses of the example structure with the smart TMD was developed. The simulation results presented that the developed DDPG model can provide effective control algorithms for smart TMD for reduction of seismic responses.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.24 no.1
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• pp.69-78
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• 2011

In this study, an optimization method using multi-objective genetic algorithm(MOGA) has been proposed to develop a fuzzy control algorithm that can effectively control a smart tuned mass damper(TMD). A 76-story benchmark building subjected to wind load was selected as an example structure. The smart TMD consists of 100kN MR damper and the natural period of the smart TMD was tuned to the first mode natural period of the example structure. Damping force of MR damper is controlled to reduce the wind-induced responses of the example structure by a fuzzy logic controller. Two input variables of the fuzzy logic controller are the acceleration of 75th floor and the displacement of the smart TMD and the output variable is the command voltage sent to MR damper. Multi-objective genetic algorithm(NSGA-II) was used for optimization of the fuzzy logic controller and the acceleration of 75th story and the displacement of the smart TMD were used as objective function. After optimization, a series of fuzzy logic controllers which could appropriately reduce both wind responses of the building and smart TMD were obtained. Based on numerical results, it has been shown that the control performance of the smart TMD is much better than that of the passive TMD and it is even better than that of the sample active TMD in some cases.

• Journal of Korean Association for Spatial Structures
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• v.21 no.1
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• pp.79-86
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• 2021

Control performance of a smart tuned mass damper (TMD) mainly depends on control algorithms. A lot of control strategies have been proposed for semi-active control devices. Recently, machine learning begins to be applied to development of vibration control algorithm. In this study, a reinforcement learning among machine learning techniques was employed to develop a semi-active control algorithm for a smart TMD. The smart TMD was composed of magnetorheological damper in this study. For this purpose, an 11-story building structure with a smart TMD was selected to construct a reinforcement learning environment. A time history analysis of the example structure subject to earthquake excitation was conducted in the reinforcement learning procedure. Deep Q-network (DQN) among various reinforcement learning algorithms was used to make a learning agent. The command voltage sent to the MR damper is determined by the action produced by the DQN. Parametric studies on hyper-parameters of DQN were performed by numerical simulations. After appropriate training iteration of the DQN model with proper hyper-parameters, the DQN model for control of seismic responses of the example structure with smart TMD was developed. The developed DQN model can effectively control smart TMD to reduce seismic responses of the example structure.

• Journal of Korean Association for Spatial Structures
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• v.11 no.4
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• pp.79-88
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• 2011

Recently, complex-shaped tall buildings represented by 3T(Twisted, Tapered, Tilted) are planed largely. A diagrid structural system is one of the most widely used structural system for complex-shaped tall buildings because of its structural efficiency and formativeness. Plans for tilted tall buildings are largely presented because of beauty of a sculpture and many of buildings use diagrid structural systems. Lateral displacements of tilted tall buildings are induced by not only lateral loads but also self weight. Therefore, reduction of lateral responses of tilted tall buildings is as important as typical tall buildings. In this study, a smart TMD is introduced to reduce seismic responses of tilted diagrid tall buildings and its control performance is evaluated. MR damper is employed for the smart TMD and ground-hook controller is used as a control algorithm for the smart TMD. 100-story tall building is used as an example structure. Control performances of uncontrolled case, controlled case with TMD and controlled case with smart TMD are compared and investigated. Numerical simulation has shown that smart TMD presented good control performance for displacement response but acceleration response was not controlled well.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.18 no.7
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• pp.720-725
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• 2017

This paper evaluated the safety and serviceability of a building structure considering the multi-hazard and proposed TMD-based adaptive smart control system to improve the structural performance. To make multi-hazard loads, an artificial earthquake and artificial wind loads were generated based on representative regions of strong seismicity and strong wind in U.S.A. The safety and serviceability of a 20-story example building structure were investigated using the generated artificial loads. A smart TMD was employed to improve the safety and serviceability of the example structure and its capacity of structural performance improvement was evaluated. The smart TMD was comprised of a MR (magnetorheological) damper. Numerical analysis showed that the example building structure could not satisfy the design limit of safety and serviceability with respect to multi-hazard. The smart TMD effectively reduced the seismic responses associated with the safety and wind-induce responses associated with serviceability.

• Journal of Korean Association for Spatial Structures
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• v.16 no.4
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• pp.91-100
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• 2016

A retractable-roof spatial structure is frequently used for a stadium and sports hall. A retractable-roof spatial structure allows natural lighting, ventilation, optimal conditions for grass growth with opened roof. It can also protects users against various weather conditions and give optimal circumstances for different activities. Dynamic characteristics of a retractable-roof spatial structure is changed based on opened or closed roof condition. A tuned mass damper (TMD) is widely used to reduce seismic responses of a structure. When a TMD is properly tuned, its control performance is excellent. Opened or closed roof condition causes dynamic characteristics variation of a retractable-roof spatial structure resulting in off-tuning. This dynamic characteristics variation was investigated. Control performance of a passive TMD and a smart TMD were evaluated under off-tuning condition.

• Smart Structures and Systems
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• v.33 no.6
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• pp.415-430
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• 2024

To control vertical and lateral compound vibration simultaneously using an integrated smart controller, passive tuned mass damper (TMD) and tuned liquid damper (TLD) are updated and combined to an adaptive-passive TMD-TLD (AP-TMD-TLD) system. As for the vertical AP-TMD part on top of the vertical spring, it can retune itself through varying the level of liquid in the tank to adjust its mass, while the lateral AP-TLD part at the bottom of the vertical spring can retune itself by changing the level of liquid. Further, for multimodal response control, the multiple AP-TMD-TLD (MAP-TMD-TLD) system is proposed as well. Each AP-TMD-TLD in the system can identify the structural vertical and lateral modal frequencies through the wavelet-transform (WT) based algorithm and retune its vertical and lateral natural frequencies both through adjusting the level of liquid in the AP-TMD and AP-TLD parts respectively. A cantilever cable-stayed landscape bridge which is sensitive to both human-induced and wind-induced vibrations is presented as a case study. For comparison, initial parameters of MAP-TMD-TLD are mistuned. Results show that the presented system can retune its vertical and lateral frequencies precisely, while the retuned system has a better bi-directional compound control effect than the mistuned system before the retuning operation and can improve the serviceability significantly.

• Journal of Korean Association for Spatial Structures
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• v.22 no.2
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• pp.39-46
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• 2022

Recently, machine learning is widely used to solve optimization problems in various engineering fields. In this study, machine learning is applied to development of a control algorithm for a smart control device for reduction of seismic responses. For this purpose, Deep Q-network (DQN) out of reinforcement learning algorithms was employed to develop control algorithm. A single degree of freedom (SDOF) structure with a smart tuned mass damper (TMD) was used as an example structure. A smart TMD system was composed of MR (magnetorheological) damper instead of passive damper. Reward design of reinforcement learning mainly affects the control performance of the smart TMD. Various hyper-parameters were investigated to optimize the control performance of DQN-based control algorithm. Usually, decrease of the time step for numerical simulation is desirable to increase the accuracy of simulation results. However, the numerical simulation results presented that decrease of the time step for reward calculation might decrease the control performance of DQN-based control algorithm. Therefore, a proper time step for reward calculation should be selected in a DQN training process.