• Journal of the Korea Institute of Building Construction
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• v.21 no.4
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• pp.361-375
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• 2021

Seoul metropolitan government of South Korea has large population and facilities, and lots of building construction have been performed in urban area. Many safety accidents with causing lives and property damages occurred at construction sites in South Korea and Seoul. Thus, Korea government and Seoul have made efforts to secure the safety of construction sites. In this study, current law and measures by central department and Seoul were analyzed to find limitations and improvement points in construction safety management. Safety inspection results for construction sites in Seoul were investigated to demonstrate the main hazard work and safety risk, and interview surveys targeting safety managing persons were performed according to safety management, inspection, and education. Based on analytical results of current status on safety management, improvement measures were proposed for safety secure on construction safety in Seoul. The proposed measures include improvement of safety management systems, alleviation of safety blind spots according to construction size, strengthening efficiency of safety management, and autonomous safety engagement by construction sites centered.

• 건축구조
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• v.21 no.3
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• pp.68-74
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• 2014

• Korean Architects
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• no.8 s.388
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• pp.85-91
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• 2001

10년도 넘었을까 모처럼 유럽을 여행할 기회가 있었다. 우리나라는 온 국토가 공사장이나 진배없던 시절이라 그곳의 풍경이 오히려 이채로웠다. 자동차를 타고 한참을 가야 그것도 별로 크지 않은 공사장을 볼 수 있었고, 그나마 신축공사 보다는 오래된 건물을 보수하는 것들이 더 많았다. 더구나 희한한 것은 대로에 면한 고색 창연한 건물의 외벽만을 텔레비전 세트처럼 남겨두고 그 뒤편에서는 전혀 새로운 공사를 하는 것도 눈에 들어왔다. 조상들의 유적을 외관만이라도 보존하도록 법제화되었으므로 여기저기 떨어져 위치한 100년도 넘은 집에다 따로따로 최신형 공작기계를 설치한 공장에서 제품을 생산하는 비능률을 감수하는 것도 볼 수 있었다. 물론 변두리 신시가지에서는 현대 감각이 물씬한 건물도 신축하고 있었는데, 대체로 리모델링 사업규모가 전체 건축 물량의 50% 정도는 될 것이라는 설명이었다. 필자는 그때 우리도 머지 않아 그런 시절이 도래할 것이라는 확신이 있었다. 더구나 그들의 건물들이 100년 이상을 버티는데 반하여 우리네 것들은 수명이 고작 20~30년 밖에 되지 못할 정도로 부실하기 때문이다. 그때부터 기존 건물의 내력 부족분을 보완하고 용도를 변경하거나 증축을 할 때 어떤 방법이 합리적일까 하는 것에 관심을 가지고 연구하게 되었다. 바닥 슬래브나 보를 잘라버리거나 새로 덧붙이기도 하고 심하면 기둥을 솎아낼 필요가 있을 수도 있다. 그러나 유감스럽게도 대부분의 경우는 그동안 열심히 고안한 방식을 실무현장에 적용할 수 있도록 받아들이는 당사자가 별로 없었다. 첫째 이유는 처음하는 일이라 말하자면 겁이 난다는 것이고, 둘째로는 새로운 방식을 믿고 시행하려 해도 실제로 책임지고 시공하겠다고 나서는 업체를 만날 수 없어서였다. 그래서 그동안 설계뿐만 아니라 시공까지 일일이 간섭하여 어렵게 시행했던 현장 경험들을 소개하여 앞으로 비슷한 조건을 만날 경우 참고할 수 있게 한다. 우선 기둥을 솎아 내는 것을 알아보고 앞으로 기둥, 보, 슬래브, 기초 등의 새로운 보강방법을 소개하고자 한다.

• Proceedings of the Korean Institute of Building Construction Conference
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• 2022.04a
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• pp.150-151
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• 2022

Blasting is a method that uses explosives to crush the ground. This method is a highly efficient ground cleaning method that can secure high efficiency in a short time. However, explosions can damage local properties and produce high noise and vibration. Therefore, it is important to be careful because there are disadvantages such as the occurrence of many complaints from the surrounding area. In this paper measured and analyzed the noise and vibration generated during blasting at the blasting site in Korea. The noise and vibration generated during blasting were measured by ES03303.2b and ES03402.2a at a distance of 6 m, 12 m from the blasting point. As a result of the measurement, there was little difference between small and medium scale except for precision vibration blasting at a distance of 6m, but noise difference according to blasting scale was evident at a distance of 12m. As a result of the measurement, the maximum noise level was reduced to 5.5 dB(A) and the vibration level was reduced to 7.7 dB(V). In the future, the reliability of the test results can be further improved through additional tests, and it is judged that noise and vibration prediction models based on blasting methods, amount of charge, measuring distance, etc. can be developed.

• Journal of the Society of Disaster Information
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• v.7 no.3
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• pp.220-228
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• 2011

Disaster sign data is confirmed data by the experts to the collected data from web and users. In this paper, we focused to make the risk scores to the data based on ontology technology. To analyse the data, first of all, we defined the ontological structure for 4 kinds of disaster types which consists of the bridges, workplaces, buildings, and walls. Base on the ontologies, collected the accidents examples, and then extract the risk rules from the examples. The rules are adjusted with frequencies and weights, and managed to the ontology DB. The rules apply to the disaster sign data, and then calculates the risk scores. It plays role of the index to the risk rates. The disaster sign data management system was implemented and the rules were verified to the system. Because the quality of the risk scores to the disaster sign data depends on the data of the accidents examples's qualities, we assure that the system's performance will be monotonic increasing following up the data upgrades. Continuously, data management is needed. Also the quality control of the rules are needed.

• The Journal of the Korea institute of electronic communication sciences
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• v.17 no.3
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• pp.473-482
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• 2022

Wearing a mask is also necessary to limit the risk of infection in today's era of COVID-19 and wearing a helmet is inevitable for the safety of personnel who works in a dangerous working environment such as construction sites. This paper proposes an effective deep learning model, HelmetMask-Net, to classify both Helmet and Mask. The proposed HelmetMask-Net is based on CNN which consists of data processing, convolution layers, max pooling layers and fully connected layers with four output classifications, and 4 classes for Helmet, Mask, Helmet & Mask, and no Helmet & no Mask are classified. The proposed HelmatMask-Net has been chosen with 2 convolutional layers and AdaGrad optimizer by various simulations for accuracy, optimizer and the number of hyperparameters. Simulation results show the accuracy of 99% and the best performance compared to other models. The results of this paper would enhance the safety of personnel in this era of COVID-19.

• 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.