Modular systems have been mostly researched in relatively low-rise structures but, lately, their applications to mid- to high-rise structures began to be reviewed, and research interest in new modularization subjects has increased. The application of modular systems to mid- to high-rise structures requires the structural stability of the frame and connections that consist of units, and the evaluation of the stiffness of structures that are combined in units. However, the combination of general units causes loss of the cross-section of columns or beams, resulting in low seismic performance and hindering installation works in the field. In addition, the evaluation of a frame considering such a cross-sectional loss is not easy. Therefore, it is necessary to develop a joint that is stable and easy to install. In the study, a rigidly connected modular system was proposed as a moment-resisting frame for a unit modular system, and their joints were developed and their performances were compared. The proposed system changed the ceiling beam into a bracket type to fasten bolts. It can be merged with other seismic force-resisting systems. To verify the seismic performance of the proposed system, a cyclic loading test was conducted, and the rigidly connected joint performance and integrated behavior at the joint of modular units were investigated. From the experimental results, the maximum resisting force of the proposed connection exceeded the theoretical parameters, indicating that a rigid joint structural performance could be secured.

The steel frame reinforced with steel shear wall is a lateral load resisting system and has higher strength and shear performance than the concrete shear wall system. Especially, using corrugated steel plates in these shear wall systems improves out-of-plane stiffness and flexibility in the deformation along the corrugation. In this paper, a cyclic loading test of this steel frame reinforced with trapezoidal-corrugated steel plate was performed to evaluate the structural performance. The hysteresis behavior and the energy dissipation capacity of the steel frame were also compared according to the corrugated direction of the plate. For the test, one simple frame model without the wall and two frame models reinforced with the plate are considered and designed. The test results showed that the model reinforced with the corrugated steel plate had a greater accumulated energy dissipation capacity than the experimental result of the non-reinforced model. Furthermore, the energy dissipation curves of two reinforced frame models, which have different corrugated directions, produced similar results.

With the development of various metaheuristic algorithms, research cases that perform weight optimization of truss structures are steadily progressing. In particular, due to the possibility of developing quantum computers, metaheuristic algorithms combined with quantum computation are being developed. In this paper, the QbHS (Quantum based Harmony Search) algorithm was proposed by combining quantum computation and the conventional HS (Harmony Search) algorithms, and the size and topology optimization of the truss structure was performed. The QbHS algorithm has the same repetitive computational structure as the conventional HS algorithm. However, the QbHS algorithm constructed $QHM$ (Quantum Harmony Memory) using the probability of Q-bit and proposed to perform pitch adjusting using the basic state of Q-bit. To perform weight optimization of truss structures using the proposed QbHS algorithm, 20 bar, 24 bar, and 72-bar truss structures were adopted as examples and compared with the results of the QE (Quantum Evolutionary) algorithm. As a result, it was confirmed that the QbHS algorithm had excellent convergence performance by finding a lower weight than the QE algorithm. In addition, by expressing the weight optimization results of the truss structure with an image coordinate system, the topology of the truss structure could be confirmed only by the picture. The results of this study are expected to play an important role in future computer information systems by combining quantum computation and conventional HS algorithms.

The conventional crow search (CS) algorithm is a swarm-based metaheuristic algorithm that has fewer parameters, is easy to apply to problems, and is utilized in various fields. However, it has a disadvantage, as it is easy for it to fall into local minima by relying mainly on exploitation to find approximations. Therefore, in this paper, we propose the advanced crow search (ACS) algorithm, which improves the conventional CS algorithm and solves the global optimization problem. The ACS algorithm has three differences from the conventional CS algorithm. First, we propose using dynamic $AP$ (awareness probability) to perform exploration of the global region for the selection of the initial population. Second, we improved the exploitation performance by introducing a formula that probabilistically selects the best crows instead of randomly selecting them. Third, we improved the exploration phase by adding an equation for local search. The ACS algorithm proposed in this paper has improved exploitation and exploration performance over other metaheuristic algorithms in both unimodal and multimodal benchmark functions, and it found the most optimal solutions in five engineering problems.