• Journal of the Korea Concrete Institute
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• v.21 no.3
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• pp.327-335
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• 2009

In the seismic region, non-ductile structures often form soft story and exhibit brittle collapse. However, structure demolition and new structure construction strategies have serious problems, as construction waste, environmental pollution and popular complain. And these methods can be uneconomical. Therefore, to satisfy seismic performance, so many seismic retrofit methods have been investigated. There are some retrofit methods as infill walls, steel brace, continuous walls, buttress, wing walls, jacketing of column or beam. Among them, the infilled frames exhibit complex behavior as follows: flexible frames experiment large deflection and rotations at the joints, and infilled shear walls fail mainly in shear at relatively small displacements. Therefore, the combined action of the composite system differs significantly from that of the frame or wall alone. Purpose of research is evaluation on the seismic performance of infill walls, and improvement concept of this paper is use of SHCCs (strain-hardening cementitious composites) to absorb damage energy effectively. The experimental investigation consisted of cyclic loading tests on 1/3-scale models of infill walls. The experimental results, as expected, show that the multiple crack pattern, strength, and energy dissipation capacity are superior for SHCC infill wall due to bridging of fibers and stress redistribution in cement matrix.

• Journal of Advanced Navigation Technology
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• v.23 no.1
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• pp.84-89
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• 2019

This is a pilot study of machine learning based structural health monitoring system using flight data of composite aircraft. In this study, the most suitable machine learning algorithm for structural health monitoring was selected and dimensionality reduction method for application on the actual flight data was conducted. For these tasks, impact test on the cantilever beam with added mass, which is the simulation of damage in the aircraft wing structure was conducted and classification model for damage states (damage location and level) was trained. Through vibration test of cantilever beam with fiber bragg grating (FBG) sensor, data of normal and 12 damaged states were acquired, and the most suitable algorithm was selected through comparison between algorithms like tree, discriminant, support vector machine (SVM), kNN, ensemble. Besides, through neighborhood component analysis (NCA) feature selection, dimensionality reduction which is necessary to deal with high dimensional flight data was conducted. As a result, quadratic SVMs performed best with 98.7% for without NCA and 95.9% for with NCA. It is also shown that the application of NCA improved prediction speed, training time, and model memory.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.50 no.7
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• pp.471-478
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• 2022

This paper is about research and development of Korea Aerospace University's Solar-Powered UAV System that named of KAU-SPUAV, and describes the design process of the 4.2 m solar UAV that succeeded in a long flight of 32 hours and 19 minutes at June 2020. In order to improve the long-term flight performance of the KAU-SPUAV, For reduce drag, a circular cross-section of the fuselage was designed, and manufactured light and sturdy fuselage by applying a monocoque structure using a glass fiber composite material. In addition, a solar module optimized for the wing shape of a 4.2 m solar drone was constructed and arranged, and a propulsion system applied with the 23[in] × 23[in] propeller was constructed to improve charging and flight efficiency. The developed KAU-SPUAV consumes an average of 55W when cruising and can receive up to 165W of energy during the day, and its Long-term Endurance was verified through flight tests.