• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2008.04a
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• pp.42-45
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• 2008

다목적 공간 및 공연장은 공연 또는 강연을 위한 공간뿐만 아니라 상점, 식당 및 체육 공간 등 다양한 용도의 공간이 인접되고 있다. 다양한 공간이 인접할 경우 기존의 다목적 공연장에서는 발생하지 않았던 다양한 소음 진동 문제들이 발생한다. 본 연구는 강연 및 공연을 위한 다목적 공간 상부에 식당 및 체육 시설로 활용되는 공간이 배치되어 바닥충격음 문제가 발생할 가능성이 있었다. 바닥충격음의 강당으로의 전달을 차단하기 위하여 기본 구조체에 대한 바닥충격음 차단성능을 측정하여 적절한 저감 대책을 수립하였다. 저감대책으로는 상층부 철골 슬래브와 강당의 철골 구조의 구조적 분리, 유연한 마감재 적용, 슬래브로의 충격진동 차단을 위한 제진재 적용, 이중천장 구조 및 천장내부 흡음구조 형성 등의 종합적인 대책을 수립하였다. 이를 통해 경량충격음 및 중량충격음을 효율적으로 차단하였다. 또한 강당 공간은 강연 및 집회의 공간으로 활용될 수 있도록 하기 위하여 적절한 잔향시간 및 음성 명료도 확보가 필요하다. 플러터 에코, 과도한 잔향시간, 음의 사각지대 등을 최소화하기 위하여 측벽 형태 개선, 천장 반사판 형태를 개선하였다. 또한 측벽에서 진동 등의 영향으로 발생되는 떨림현상을 억제하기 위하여 측벽 내부를 흡음재로 충전하는 등의 대책을 수립하여 적용하였다.

• Transactions of the Korean Society for Noise and Vibration Engineering
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• v.22 no.2
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• pp.130-138
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• 2012

Environmental noise in the residential area is appeared with different types and features along with the growth of society. In particular when the economic level is raised up, the demand of residents for the environmental noise becomes more sensitive. The existing research on the environmental noise was initiated in the 1980's and resulted that the children's jumping sound was most annoying among the noises transmitted from the other unit. This result caused such activities that the researches were focused on the floor impact noise and that the laws in relation to environmental noise within residential area were revised strictly. This study aimed to survey the consciousness of residents for the environmental noise and to see how it is changed comparing with the previous one. Ultimately this result could be useful to establish the target for research or noise control to make the comfortable residential environment.

• Proceedings of the Korean Institute of Building Construction Conference
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• 2006.11a
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• pp.57-60
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• 2006

This study proposes the physical properties of the hardened lightweight cement using the polyethylene tube and to make the fundamental data regarding a new lightweight concrete development. The aerated concrete is displaying various effects such as lightweight, insulation characteristic and it is coming to be widely applied the slab layer of apartment as an insulating material but currently the aerated concrete has many problems. Therefore, demonstrating similar property of former aerated concrete and improving the defects, developing new hardened cement is needed. In this study, we predict adopting possibility of hollow core polyethylene tube, as a material to make cement hardening containing a lot of void. So we changed the mixing ratio, a diameter and length of the polyethylene tube and improved the compressive strength and unit capacity weight of the lightweight cement hardening body. From the test results, we judge that the aerated concrete is a developmental possibility.

• The Journal of the Acoustical Society of Korea
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• v.42 no.6
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• pp.603-616
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• 2023

In this study, machine learning-based clustering and classification of residential noise in apartment buildings was conducted using frequency and temporal characteristics. First, a residential noise source dataset was constructed . The residential noise source dataset was consisted of floor impact, airborne, plumbing and equipment noise, environmental, and construction noise. The clustering of residential noise was performed by K-Means clustering method. For frequency characteristics, L_eq and L_max values were derived for 1/1 and 1/3 octave band for each sound source. For temporal characteristics, Leq values were derived at every 6 ms through sound pressure level analysis for 5 s. The number of k in K-Means clustering method was determined through the silhouette coefficient and elbow method. The clustering of residential noise source by frequency characteristic resulted in three clusters for both L_eq and L_max analysis. Temporal characteristic clustered residential noise source into 9 clusters for L_eq and 11 clusters for L_max. Clustering by frequency characteristic clustered according to the proportion of low frequency band. Then, to utilize the clustering results, the residential noise source was classified using three kinds of machine learning. The results of the residential noise classification showed the highest accuracy and f1-score for data labeled with L_eq values in 1/3 octave bands, and the highest accuracy and f1-score for classifying residential noise sources with an Artificial Neural Network (ANN) model using both frequency and temporal features, with 93 % accuracy and 92 % f1-score.