• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2006.05a
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• pp.1421-1424
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• 2006

Recently Requirement of indoor environment is increased in APT. Among indoor noises of APT, noise of plumbing system in bathroom is very serious problem except of floor impact noise. Plumbing system with wan hanging unit make a good grade and recognition in rating noise of bathroom in grade of house rating. But it is hard to find a data which are measured in APT built. In this study, the effect of noise reduction is checked by measuring the noise of plumbing system with wall hanging unit that is built. As result the upper household's Peak sound level is measured 80dB(A), the under household's peak sound level is measured 40dB(A).

• Transactions of the Korean Society for Noise and Vibration Engineering
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• v.18 no.6
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• pp.663-673
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• 2008

The purpose of this study is to analyze the appropriate evaluation method for the water supply and drain noise of bathroom in apartment and to propose some fundamental idea on its regulation. For this reason, it was studied the acoustical characteristics of water supply and drain noise as modifying the water supply pressure for the washbasins and toilet stools and suggested the optimum evaluation method through psychoacoustic test. As a result of investigation of the levels by evaluating the adjectives and noise sources with 7-step criteria corresponding to each level of plumbing noises and analysis of the correlation between physical evaluation values by comparing the criteria, dB(A), N, and NC, with subject's response, determination coefficient($R^2$) was shown to be relatively fair or higher ranged from 0.65 to 0.92. It is shown that 'Harsh', 'Nervous', 'Unpleasant', 'Distasteful', and 'Repellent' as a second factor are to be appropriate to evaluate plumbing noise. Above these results could be used in basic data establishing KS(Korean Standard) for evaluation and rating procedure and measures reducing these noise.

• KIEAE Journal
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• v.7 no.1
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• pp.57-64
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• 2007

This study aims to propose appropriate evaluating method of noise emission from water supply and drain installations in apartment bathroom. The measurement procedure of water installations noise in apartment bathroom were established, so it is necessary to provide the evaluating method and criteria to improve the sound insulation performance of the apartment and to reduce the apartment dweller's unsatisfaction with the noise. Thus, this study reviewed the standards and evaluating methods about the water supply and drain installations noise of many other country. After measuring the noise emission from the installations in many apartment bathroom, The noise were evaluated dB(A), N, NC index, To induce the appropriate method, the coefficient of the correlation among the evaluated numbers were analyzed. As a result, the dB(A) method is most easy to evaluating and very high correlated with N and N index, So the dB(A) method is suitable to be adopted KS evaluating procedure of noise emission from water installations in apartment bathroom.

• 월간 기계설비
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• s.47
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• pp.74-84
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• 1994

본 연구원에서는 삼성건설에서 생산시공되고 있는 조립식주택 부재에 대하여 공업화주택 성능인정 기준과 관련된 항목중 단열, 차음, 내화성능등의 성능을 분석$\cdot$평가하여 PC조립식부재에 대한 기술자료를 마련하고자 하였다. 또한, 기존 조립식주택을 대상으로 주거환경과 직접적인 영향을 미치는 벽체 및 적합부의 단열 및 결로, 공간차음성능, 바닥충격음 차단성능, 급배수설비 소음 성능등에 대한 실물실험 및 현장실측조사를 통해 파악함으로써 조립식주택의 주건환경을 분석$\cdot$평가하였다. 각 항목별 성능에 대한 종합적인 분석결과, PC아파트의 기존의 현장타설 RC아파트는 주거성능 측면에서 별다른 차이를 보이지 않았으며, 단열성능 등 몇가지 요소에서 PC아파트가 상대적으로 우수한 것으로 분석$\cdot$평가되었다.

• 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.