• Smart Structures and Systems
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• v.10 no.1
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• pp.33-46
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• 2012

To reduce the vibration of cable-stayed bridges, conventional magnetorheological (MR) damper control system (CMRDS), with separate power supply, sensors and controllers, is widely investigated. In this paper, to improve the reliability and performance of the control system, one adaptive MR damper control system (AMRDS) consisting of MR damper and piezoelectric energy harvester (PEH) is proposed. According to piezoelectric effect, PEH can produce energy for powering MR damper. The energy is proportional to the product of the cable displacement and velocity. Due to the damping force changing with the energy, the new system can be adjustable to reduce the cable vibration. Compared with CMRDS, the new system is structurally simplified, replacing external sensor, power supply and controller with PEH. In the paper, taking the N26 cable of Shandong Binzhou Yellow River Bridge as example, the design method for the whole AMRDS is given, and simple formulas for PEH are derived. To verify the effectiveness of the proposed adaptive control system, the performance is compared with active control case and simple Bang-Bang semi-active control case. It is shown that AMRDS is better than simple Bang-Bang semi-active control case, and still needed to be improved in comparison with active control case.

• Journal of Korean Association for Spatial Structures
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• v.19 no.2
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• pp.43-50
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• 2019

A smart connective control system was invented recently for coupling control of adjacent buildings. Previous studies on this topic focused on development of control algorithm for the smart connective control system and design method of control device. Usually, a smart control devices are applied to building structures after structural design. However, because structural characteristics of building structure with control devices changes, a iterative design is required for optimal design. To defeat this problem, an integrated optimal design method for a smart connective control system and connected buildings was proposed. For this purpose, an artificial seismic load was generated for control performance evaluation of the smart coupling control system. 20-story and 12-story adjacent buildings were used as example structures and an MR (magnetorheological) damper was used as a smart control device to connect adjacent two buildings. NSGA-II was used for multi-objective integrated optimization of structure-smart control device. Numerical simulation results show the integrated optimal design method proposed in this study can provide various optimal designs for smart connective control system and connected buildings presenting good control performance.

• Smart Structures and Systems
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• v.23 no.6
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• pp.521-536
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• 2019

Using magnetorheological (MR) dampers in multiswitch open-loop control mode has been shown to be cost-effective for cable vibration mitigation. In this paper, a method for analyzing the damping performance of taut cables incorporating MR dampers in open-loop control mode is developed considering the effects of damping coefficient, damper stiffness, damper mass, and stiffness of the damper support. Making use of a three-element model of MR dampers and complex modal analysis, both numerical and asymptotic solutions are obtained. An analytical expression is obtained from the asymptotic solution to evaluate the equivalent damping ratio of the cable-damper system in the open-loop control mode. The individual and combined effects of the damping coefficient, damper stiffness, damper mass and stiffness of damper support on vibration control effectiveness are investigated in detail. The main thrust of the present study is to derive a general formula explicitly relating the normalized system damping ratio and the normalized damper parameters in consideration of all concerned effects, which can be easily used for the design of MR dampers to achieve optimal open-loop vibration control of taut cables.

• Journal of Korean Association for Spatial Structures
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• v.18 no.4
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• pp.97-104
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• 2018

A connected control method for the adjacent buildings has been studied to reduce dynamic responses. In these studies, seismic loads were generally used as an excitation. Recently, multi-hazards loads including earthquake and strong wind loads are employed to investigate control performance of various control systems. Accordingly, strong wind load as well as earthquake load was adopted to evaluate control performance of adaptive smart coupling control system against multi-hazard. To this end, an artificial seismic load in the region of strong seismicity and an artificial wind load in the region of strong winds were generated for control performance evaluation of the coupling control system. Artificial seismic and wind excitations were made by SIMQKE and Kaimal spectrum based on ASCE 7-10. As example buildings, two 20-story and 12-story adjacent buildings were used. An MR (magnetorheological) damper was used as an adaptive smart control device to connect adjacent two buildings. In oder to present nonlinear dynamic behavior of MR damper, Bouc-Wen model was employed in this study. After parametric studies on MR damper capacity, optimal command voltages for MR damper on each seismic and wind loads were investigated. Based on numerical analyses, it was shown that the adaptive smart coupling control system proposed in this study can provide very good control performance for Multi-hazards.

• Journal of Korean Association for Spatial Structures
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• v.21 no.3
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• pp.43-50
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• 2021

A tilted tall building is actively constructed as landmark structures around world to date. Because lateral displacement responses of a tilted tall building occurs even by its self-weight, reduction of seismic responses is very important to ensure structural safety. In this study, a smart tuned mass damper (STMD) was applied to the example tilted tall building and its seismic response control performance was investigated. The STMD was composed of magnetorheological (MR) damper and it was installed on the top floor of the example building. Control performance of the STMD mainly depends on the control algorithn. Fuzzy logic controller (FLC) was selected as a control algorithm for the STMD. Because composing fuzzy rules and tuning membership functions of FLC are difficult task, evolutionary optimization algorithm (EOA) was used to develop the FLC. After numerical simulations, it has been seen that the STMD controlled by the EOA-optimized FLC can effectively reduce seismic responses fo the tilted tall building.

• 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 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.21 no.4
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• pp.73-80
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• 2021

A mid-story isolation system was proposed for seismic response reduction of high-rise buildings and presented good control performance. Control performance of a mid-story isolation system was enhanced by introducing semi-active control devices into isolation systems. Seismic response reduction capacity of a semi-active mid-story isolation system mainly depends on effect of control algorithm. AI(Artificial Intelligence)-based control algorithm was developed for control of a semi-active mid-story isolation system in this study. For this research, an practical structure of Shiodome Sumitomo building in Japan which has a mid-story isolation system was used as an example structure. An MR (magnetorheological) damper was used to make a semi-active mid-story isolation system in example model. In numerical simulation, seismic response prediction model was generated by one of supervised learning model, i.e. an RNN (Recurrent Neural Network). Deep Q-network (DQN) out of reinforcement learning algorithms was employed to develop control algorithm The numerical simulation results presented that the DQN algorithm can effectively control a semi-active mid-story isolation system resulting in successful reduction of seismic responses.

• 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.

• Ocean Systems Engineering
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• v.13 no.4
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• pp.401-421
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• 2023

Reducing hawser line tensions and dynamic responses to a certain level is of paramount importance as the hawser lines provide important structural linkage between 2 body TLP-TAD system. The objective of this paper is to demonstrate how MR Damper can be utilized to achieve this. Hydrodynamic coefficients and wave forces for two bodies including second-order effects are obtained by 3D diffraction/radiation panel program by potential theory. Then, multi-hull-riser-mooring-hawser fully-coupled time-domain dynamic simulation program is applied to solve the complex two-body system's dynamics with the Magneto-Rheological (MR) Damper modeled on one end of hawser. Since the damping level of MR Damper can be changed by inputting different electric currents, various simulations are conducted for various electric currents. The results show the reductions in maximum hawser tensions with MR Damper even for passive control cases. The results also show that the hawser tensions and MR Damper strokes are affected not only by input electric currents but also by initial mooring design. Further optimization of hawser design with MR Damper can be done by active MR-Damper control with changing electric currents, which is the subject of the next study.