High-rise buildings are equipped with TMD (Tuned Mass Damper), a vibration control device that ensure the stability and usability of the building. In this study, the seismic response control performance was evaluated by selecting the design variables of the TMD based on the installation location of the twisted irregular building. To this end, we selected analysis models of 60, 80, and 100 floors with a twist angle of 1 degree per floor, and performed time history analysis by applying historical seismic loads and resonant harmonic loads. The total mass ratio of TMDs was set to 1.0%, and the distributed installation locations of TMDs were selected through mode analysis. The analysis results showed that the top-floor displacement responses of all analysis models increased, but the maximum story drift ratio decreased. In order to secure the seismic response control performance by distributed installation of TMDs in twisted irregular buildings, it is judged that the mass ratio distribution of TMDs will act as a key variable.

Since atypical high-rise buildings are vulnerable to gravity loads and seismic loads, various structural systems must be applied to ensure the stability of the structure. In this study, the authors selected a 60-story twisted-shaped structure among atypical high-rise structures as an analytical model to investigate its structural behavior concerning the outrigger system. The structural analyses were performed varying the number of installed layers and the arrangement of the outrigger system, as well as the placement of the mega column, as design variables. The analysis revealed that the most effective position for the outrigger was 0.455H from the top layer, consistent with previous studies. Additionally, connecting outriggers and mega columns significantly reduced the displacement response of the model. From an economic standpoint, it is deemed efficient to connect and install outriggers and mega columns at the structure's ends.

In this study, we investigated the dynamic characteristics of three irregular building models to analyze the effectiveness of displacement response control with Tuned Mass Damper (TMD) installation in twisted irregular buildings. The three irregular models were developed with a fixed angle of twist per story at one degree, subjected to three historical seismic loads and resonant harmonic loads. By designing TMDs with linear and dashpot attributes, we varied the total mass ratio of the installed TMDs from 0.00625% to 1.0%, encompassing a total of 10 values. Two TMDs were installed at the center of the top story of the analysis model in both X and Y directions to evaluate displacement response control performance based on TMD installation. Our findings suggest that the top displacement response control performance was most effective when a 1.0% TMD was installed at the top layer of the analysis model.

In this study, the seismic response characteristics of the three analysis model with or without TMD were investigated to find out the effective dome shape. The three analysis models are rib type, lattice type and geodesic type dome structure composed of space frame. The maximum vertical and horizontal displacements were evaluated at 1/4 point of the span by applying the resonance harmonic load and historical earthquake loads (El Centro, Kobe, Northridge earthquakes). The study of the effective TMD installation position for the dome structure shows that seismic response control was effective when eight TMDs were installed in all types of analysis model. The investigation of the efficiency of TMD according to dome shape presents that lattice dome and geodesic dome show excellent control performance, while rib dome shows different control performance depending on the historical seismic loads. Therefore, lattice and geodesic types are desirable for seismic response reduction using TMD compared to rib type.

Recently, Free-Form and Irregular Shape high-rise buildings are constructed by IT technology development. Tilted shaped high-rise building which is one of Irregular shape high-rise buildings can cause lateral displacement by gravity load and lateral load due to tilted elevation shape. Therefore, it is necessary to review the behavior and structural aspects of the Tilted shape high-rise building by gravity load. In this paper, the dynamic characteristics of a tilted structure with a dual-core were analyzed with the core location as a design variable, and response behavior, vulnerable members, and vulnerable layers to earthquake loads were analyzed. As a result of the analysis, as the location of the core moved in an tilted direction, the eccentric distance and eccentric load decreased, reducing the axial force of the vertical members. However, the location of the core had little effect on the response.

Reinforcement learning (RL) is widely applied to various engineering fields. Especially, RL has shown successful performance for control problems, such as vehicles, robotics, and active structural control system. However, little research on application of RL to optimal structural design has conducted to date. In this study, the possibility of application of RL to structural design of reinforced concrete (RC) beam was investigated. The example of RC beam structural design problem introduced in previous study was used for comparative study. Deep q-network (DQN) is a famous RL algorithm presenting good performance in the discrete action space and thus it was used in this study. The action of DQN agent is required to represent design variables of RC beam. However, the number of design variables of RC beam is too many to represent by the action of conventional DQN. To solve this problem, multi-agent DQN was used in this study. For more effective reinforcement learning process, DDQN (Double Q-Learning) that is an advanced version of a conventional DQN was employed. The multi-agent of DDQN was trained for optimal structural design of RC beam to satisfy American Concrete Institute (318) without any hand-labeled dataset. Five agents of DDQN provides actions for beam with, beam depth, main rebar size, number of main rebar, and shear stirrup size, respectively. Five agents of DDQN were trained for 10,000 episodes and the performance of the multi-agent of DDQN was evaluated with 100 test design cases. This study shows that the multi-agent DDQN algorithm can provide successfully structural design results of RC beam.

Tuned mass damper (TMD) is widely used to reduce dynamic responses of structures subjected to earthquake loads. A smart tuned mass damper (STMD) was proposed to increase control performance of a traditional passive TMD. A lot of research was conducted to investigate the control performance of a STMD based on analytical method. Experimental study of evaluation of control performance of a STMD was not widely conducted to date. Therefore, seismic response reduction capacity of a STMD was experimentally investigated in this study. For this purpose, a STMD was manufactured using an MR (magnetorheological) damper. A simple structure presenting dynamic characteristics of spacial roof structure was made as a test structure. A STMD was made to control vertical responses of the test structure. Two artificial ground motions and a resonance harmonic load were selected as experimental seismic excitations. Shaking table test was conducted to evaluate control performance of a STMD. Control algorithms are one of main factors affect control performance of a STMD. In this study, a groundhook algorithm that is a traditional semi-active control algorithm was selected. And fuzzy logic controller (FLC) was used to control a STMD. The FLC was optimized by multi-objective genetic algorithm. The experimental results presented that the TMD can effectively reduce seismic responses of the example structures subjected to various excitations. It was also experimentally shown that the STMD can more effectively reduce seismic responses of the example structures conpared to the passive TMD.

When the center of stiffness and the center of mass of the structure differ under the seismic load, torsion is caused by eccentricity. In this study, an analysis model was modeled in which the positions of the core and the plane rotation axis of a 60-story torsional atypical structure with a plane rotation angle of 1 degree per floor were different. The structural behavior of the analysis model was analyzed, and the earthquake response behavior of the structure was analyzed based on the time history analysis results. As a result, as the eccentricity of the structure increased, the eccentricity response was amplified in the high-rise part, and the bending and torsional behavior responses were complex in the low-order vibration mode. As a result of the analysis, the maximum displacement and story drift ratio increased due to the torsional behavior. The maximum story shear force and the story absolute maximum acceleration showed similarities for each analysis model according to the shape of the vibration mode of the analysis model.

Currently, the construction trend of high-rise structures is changing from a cube-shaped box to a free-form. In the case of free-form structures, it is difficult to predict the behavior of the structure because it induces torsional deformation due to inclined columns and the eccentricity of the structure by the horizontal load. For this reason, it is essential to review the stability by considering the design variables at the design stage. In this paper, the position of the weak vertical member was analyzed by analyzing the behavior of the structure according to the change in the core position of the twisted high-rise structures. In the case of the shear wall, the shear force was found to be high in the order of proximity to the center of gravity of each floor of the structure. In the case of the column, the component force was generated by the axial force of the outermost beam, so the bending moment was concentrated on the inner column with no inclination.

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.

In this paper, a twisted shape structure with an elevation form favorable to the resistance of vibration caused by wind loads is selected from among the forms of high-rise buildings. The analytical model is a square, triangular, and hexagonal plane with a plane rotation angle of one degree from 0 to 3 degrees per each story. As a result of the analysis, as the twist angle increased, story drift ratio is increased. Responses with different eccentricity rates were shown by analytical models. Therefore planar shapes designed symmetrically to the horizontal axis of X and Y are considered advantageous for eccentricity and torsion deformation. In the case of the bending moment of the column, the response was amplified in the column supporting the base floor, the roof floor, the floor in which the cross-section of the vertical member changes, and the floor having the same number of nodes as the base floor. Finally, the axial force response of the column is determined to be absolutely affected by the gravity load compared to the lateral load.

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.

인디카 벼는 일조시간이 짧은 열대지역에서 주로 재배하며, 자포니카 벼는 일조시간이 긴 한국, 일본, 및 중국(동북부 지역)을 포함하는 온대지역에서 재배한다. 최근 동남아 열대지역에서 자포니카 쌀에 대한 수요가 증가함에 따라 농촌진흥청은 필리핀 국제미작연구소(IRRI)와 공동으로 열대지역에 적응하는 다수성 온대 자포니카 벼 품종을 개발하고 있다. 일반적으로 자포니카 벼를 단일조건인 열대지역에 재배하면 이앙 후 바로 개화가 촉진되어 극조기 출수가 유도된다. 따라서 열대지역에 적응하는 자포니카 벼를 개발하기 위해서는 단일조건에서도 충분히 생장한 후 출수를 하는 특성이 우선적으로 필요하다. 본 연구는 국내 자포니카형 벼 품종인 ‘일품’과 미국에서 육성한 인디카형 벼인 ‘Zenith’를 교배한 F9 RIL 180 계통을 이용하여 필리핀의 단일조건하에서 재배하면서 출수기에 관련한 양적형질유전자좌(QTL) 분석을 수행한 바, 그 결과는 다음 과 같다. 1. 단일조건하에서 출수일수(파종~출수)에 관여하는 2종의 QTL(qHD6-SD와 qHD6-LD)을 탐색하였다. 2. 정밀지도제작 결과 qHD6-SD와 qHD6-LD은 모두 6번 염색체 Hd1 유전자를 포함하는 98 kb 영역에 존재하는 동일한 QTL인 것으로 나타났다 3. 시험계통을 필리핀 단일 조건에서 재배하였을 때 qHD6- SD 또는 qHD6-LD에서 Zenith allele형을 보유한 계통들은 일 품 allele형을 가진 계통들 보다 출수일수가 평균 8일 정도 길었다. 3. 시험계통을 한국의 장일 조건에서 재배하였을 때 qHD6- SD 또는 qHD6-LD에서 Zenith allele형을 보유한 계통들은 일품 allele형을 가진 계통들보다 출수일수가 평균 8일 정도 짧 았다. 4. 이러한 특성은 기존에 보고된 Hd1 유전자의 특성과 유사하여 qHD6-SD 및 qHD6-LD 은 Hd1 유전자일 가능성이 높은 것으로 나타났으며, 본 시험에서 사용한 Zenith는 nonfunctional Hd1 allele을 보유하는 것으로 추정되었다. 5. 이러한 결과를 통해 열대지역에 적응하는 자포니카 벼 육종연구에서 극조기 출수를 방지하고 충분한 출수일수를 확보하여 수량을 높이기 위해서는 인디카 벼가 주로 보유하고 있는 non-functional Hd1 allele을 반드시 도입해야 함을 재확인 하였다.

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.

In this paper, the dynamic response was analyzed by performing linear dynamic analysis using historic earthquake loads on twisted-shaped structures and fixed structure among free-form high-rise structures with atypical elevation shape following prior studies. In addition, the dynamic characteristics of the analysis models according to the plane rotation angle of the twisted structure were compared and analyzed. As a result of the analysis, as the plane rotation angle of the twisted structure increased, the interlayer deformation rate increased in the high-rise part of 50th floors or more. The story shear force and the story absolute acceleration were similar in the entire structure. In the case of the story shear force, the response of the twisted shape model was rather reduced in the middle part. As a result of analyzing the dynamic response, the vulnerable layer where the response amplification of the twisted structure occurs was found to be 31st story.

As people's living standards and cultural standards have developed, interest in culture and art has increased, and the demand for large space structures where people can enjoy art, music, and sports has increased. As it accommodates a large number of personnel, it is most important to ensure safety of large spatial structures, and can be used as a space where people can evacuate in case of a disaster. Large spatial structures should be prepared for earthquake loads rather than wind loads. In addition to damage to the structure due to earthquakes, there are cases in which it was not utilized as a space for evacuation due to the fall of objects installed on top of the structure. Therefore, in this study, the dome-shaped large spatial structure is generalized and the displacement response according to the number of installations, position and mass is analyzed using a tuned mass damper(TMD) that is representative vibration control device.

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.