• Journal of the Korea Institute of Building Construction
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• v.24 no.1
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• pp.121-131
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• 2024

This research explores the potential of leveraging unstructured data from construction supervision documents, which contain detailed inspection insights from independent third-party monitors of building construction processes. With the evolution of analytical methodologies, such unstructured data has been recognized as a valuable source of information, offering diverse insights. The study introduces a framework designed to assess cost performance by applying advanced analytical methods to the unstructured data found in final construction supervision reports. Specifically, key phrases were identified using text mining and social network analysis techniques, and these phrases were then analyzed through binomial logistic regression to assess cost performance. The study found that predictions of cost performance based on unstructured data from supervision documents achieved an accuracy rate of approximately 73%. The findings of this research are anticipated to serve as a foundational resource for analyzing various forms of unstructured data generated within the construction sector in future projects.

• Proceedings of the Korea Water Resources Association Conference
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• 2019.05a
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• pp.260-260
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• 2019

해안과 만나는 하천의 하구부에서는 해수의 흐름에 따른 연안토사의 이동과 유황에 따른 하천상류에서의 토사유입량에 의해 하구부가 빠르게 변화한다. 본 연구대상지인 가곡천은 유로연장이 짧고 경사가 급한 산지하천의 특징을 가지고 있어 집중호우시 토사가 하류로 빠르게 유입되어 일반하천에 비하여 하구부의 지형변화가 크게 일어난다. 가곡천 하구부는 모래사장으로 이루어져 있는 호산해수욕장과 경관적 및 자연적 가치로 인해 보존 필요성이 대두되고 있는 하중도인 솔섬이 위치해 있다. 그러나 현재 솔섬 주변 하구부 인근의 일반산업단지 건설과 고향의 강 조성사업에 따른 하도환경의 변화로 하도를 유하하는 유수의 흐름 및 침퇴적 양상의 변화를 가져와 홍수의 원활한 소통에 지장을 초래하고 있다. 또한 현장조사 및 모니터링 결과 하구 지형변화로 인해 하도안정 유지관리에도 막대한 영향을 끼치는 것으로 확인되었다. 따라서 본 연구에서는 가곡천 하구부의 하도환경 변화에 따른 수리학적 특성 및 하천형태학적인 변화를 분석하여 하도안정을 파괴하는 요인을 규명하기 위한 연구를 수행하였다. 연구방법은 가곡천 하천정비기본계획의 빈도별 홍수량, 기점홍수위를 경계조건으로 2차원 수리해석 모형인 SRH-2D모형을 이용하여 하도환경 변화 전 후의 대상구간에 대해 수리특성 및 하상변동 양상을 모의하였다. 모의결과 하도환경 변화(홍수터 및 징검여울 등의 하천시설물 조성)에 따른 수리특성변동양상과 침 퇴적지점에 대한 변동경향을 확인할 수 있었다. 본 연구의 결과는 동해안 하구부에 위피한 하천의 다양한 변화 해석시 참고자료로 활용할 수 있을 것으로 사료된다.

• Journal of Korean Tunnelling and Underground Space Association
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• v.21 no.3
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• pp.419-432
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• 2019

Most of deep learning model training was proceeded by supervised learning, which is to train labeling data composed by inputs and corresponding outputs. Labeling data was directly generated manually, so labeling accuracy of data is relatively high. However, it requires heavy efforts in securing data because of cost and time. Additionally, the main goal of supervised learning is to improve detection performance for 'True Positive' data but not to reduce occurrence of 'False Positive' data. In this paper, the occurrence of unpredictable 'False Positive' appears by trained modes with labeling data and 'True Positive' data in monitoring of deep learning-based CCTV accident detection system, which is under operation at a tunnel monitoring center. Those types of 'False Positive' to 'fire' or 'person' objects were frequently taking place for lights of working vehicle, reflecting sunlight at tunnel entrance, long black feature which occurs to the part of lane or car, etc. To solve this problem, a deep learning model was developed by simultaneously training the 'False Positive' data generated in the field and the labeling data. As a result, in comparison with the model that was trained only by the existing labeling data, the re-inference performance with respect to the labeling data was improved. In addition, re-inference of the 'False Positive' data shows that the number of 'False Positive' for the persons were more reduced in case of training model including many 'False Positive' data. By training of the 'False Positive' data, the capability of field application of the deep learning model was improved automatically.

• Journal of the Korean Geotechnical Society
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• v.25 no.5
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• pp.53-64
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• 2009

Many slope failures are induced by rainfall infiltration. A lot of recent researches are therefore focused on rainfall-induced slope instability and the rainfall infiltration is recognized as the important triggering factor. The rainfall infiltrates into the soil slope and makes the matric suction lost in the slope and even the positive pore water pressure develops near the surface of the slope. They decrease the resisting shear strength. In Korea, a few public institutions suggested conservative slope design guidelines that assume a fully saturated soil condition. However, this assumption is irrelevant and sometimes soil properties are misused in the slope design method to fulfill the requirement. In this study, a more relevant slope stability evaluation method is suggested to take into account the real rainfall infiltration phenomenon. Unsaturated soil properties such as shear strength, soil-water characteristic curve and permeability for Korean weathered soils were obtained by laboratory tests and also estimated by artificial neural network models. For real-time assessment of slope instability, failure warning criteria of slope based on deterministic and probabilistic analyses were introduced to complement uncertainties of field measurement data. The slope stability evaluation technique can be combined with field measurement data of important factors, such as matric suction and water content, to develop an early warning system for probably unstable slopes due to the rainfall.

• Journal of the Korea Institute of Building Construction
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• v.11 no.3
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• pp.283-291
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• 2011

As the construction industry has recently been transformed by the emergence of ubiquitous and intelligent technology, there have been major changes in the management methods employed. Specifically, next-generation construction management systems have been developed that collect and analyze many pieces of information in real time by using various wireless sensors and networks. The purpose of this study is to understand the current status of Ubiquitous Sensor Networks (USN) in the construction sector, and to gain fundamental data for a system of measuring concrete curing temperature in a construction site that employs a USN. By investigating the application status of USN, it was confirmed that USN has mainly been applied to the maintenance of facilities, safety management, and quality control. In addition, a field experiment in which the curing temperature of concrete was measured using a USN was carried out to evaluate two systems with wireless sensor networks, and the applicability of these systems on site was confirmed. However, it is estimated that the embedded wireless sensor type is affected by metal equipment on site, internal battery of sensor and concrete depth, and studies to provide more stable system by USN are thus required.

• Tunnel and Underground Space
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• v.33 no.4
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• pp.281-298
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• 2023

Continuous research efforts are being devoted to unmanned mobile platforms for lunar exploration. There is an ongoing demand for real-time information processing to accurately determine the positioning and mapping of areas of interest on the lunar surface. To apply deep learning processing and analysis techniques to practical rovers, research on software integration and optimization is imperative. In this study, a foundational investigation has been conducted on real-time analysis of virtual lunar base construction site images, aimed at automatically quantifying spatial information of key objects. This study involved transitioning from an existing region-based object recognition algorithm to a boundary box-based algorithm, thus enhancing object recognition accuracy and inference speed. To facilitate extensive data-based object matching training, the Batch Hard Triplet Mining technique was introduced, and research was conducted to optimize both training and inference processes. Furthermore, an improved software system for object recognition and identical object matching was integrated, accompanied by the development of visualization software for the automatic matching of identical objects within input images. Leveraging satellite simulative captured video data for training objects and moving object-captured video data for inference, training and inference for identical object matching were successfully executed. The outcomes of this research suggest the feasibility of implementing 3D spatial information based on continuous-capture video data of mobile platforms and utilizing it for positioning objects within regions of interest. As a result, these findings are expected to contribute to the integration of an automated on-site system for video-based construction monitoring and control of significant target objects within future lunar base construction sites.

• Journal of Nuclear Fuel Cycle and Waste Technology(JNFCWT)
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• v.17 no.2
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• pp.245-261
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• 2019

Radioactive contaminant from a nuclear facility moves to the ecosystem by run-off or groundwater flow. Among the two mechanisms, contaminant plume through a river can be easily detected through a surface water monitoring system, but radioactive contaminant transport in groundwater is difficult to monitor because of lack of information on flow path. To understand the contaminant flow in groundwater, understanding of the geo-environment is needed. We suggest a method to decide on monitoring location and points around an imaginary nuclear facility by using the results of site characterization in the study area. To decide the location of a monitoring well, groundwater flow modeling around the study area was conducted. The results show that, taking account of groundwater flow direction, the monitoring well should be located at the downstream area. Also, monitoring sections in the monitoring well were selected, points at which groundwater moves fast through the flow path. The method suggested in the study will be widely used to detect potential groundwater contamination in the field of oil storage caverns, pollution by agricultural use, as well as nuclear use facilities including nuclear power plants.

• The Journal of Engineering Geology
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• v.28 no.4
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• pp.701-714
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• 2018

The purpose of this study is to show the possibility of 3-dimensional modeling of open-pit limestone mine by using a rotary-wing unmanned aerial vehicle, a drone, and to estimate the amount of mining before and after mining of limestone by explosive blasting. Analysis of the image duplication of the mine has shown that it is possible to achieve high image quality. Analysis of each axis error at the shooting position after analyzing the distortions through camera calibration was shown the allowable range. As a result of estimating the amount of mining before and after explosive blasting, it was possible to estimate the amount of mining of a wide range quickly and accurately in a relatively short time. In conclusion, it is considered that the drone of a rotary-wing unmanned aerial vehicle can be usefully used for the monitoring of open-pit limestone mines and the estimation of the amount of mining. Furthermore, it is expected that this method will be utilized for periodic monitoring of construction sites and road slopes as well as open-pit mines in the future.

• Proceedings of the Korea Water Resources Association Conference
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• 2020.06a
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• pp.30-30
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• 2020

본 연구에서는 여름철에 농업용수(벼농사용)로서 집중적으로 지하수를 사용하는 지역에서 시기별 지하수 사용에 따른 지하수 수질변화를 평가하기 위해 수행되었다. 연구지역은 충남 홍성군 양곡리와 신곡리 일부를 포함하는 면적 2.83 ㎢(283.3 ha)에 해당하는 지역이다. 연구지역 지하수 수질의 공간적 분포 및 시간적 변화 특성 평가를 위하여 2019년 2회(7월, 10월)에 걸쳐 지하수 관정(21개소)에 대하여 조사 및 분석을 수행하였다. 지하수 샘플은 현장에서 온도(T), pH, 용존산소(DO) 및 전기전도도(EC), 산화환원전위(Eh) 등을 측정하였고, 실험실에서 주요 양이온 및 미량원소(Ca, Mg, Na, K, Si, Sr), 음이온(F, Cl, Br, NO₂, NO₃, PO₄, SO₄), 알칼리도, 용존 유기탄소(DOC)와 용존 유기물(DOM) 등을 분석하였다. 지하수 수질조사 결과, 전체의 14~15개소(67~71%)가 Ca-HCO₃ 유형으로 분류되었으며, 다음으로는 Ca-Cl 유형이 4~5개소(19~24%)가 관찰되었다. 얕은 심도의 관정에서 상대적으로 심도가 깊은 관정보다 대부분 성분(TDS, Ca, Mg, Na, K, Cl, SO₄, HCO₃, DOC)에서 높은 농도를 나타내었다. 지하수의 수질자료를 이용하여 다변량통계분석법인 주성분분석(PCA: Principal Components Analysis)과 계층적 군집분석(HCA: Hierachical Cluster Anlaysis)를 수행한 결과, 초기 3개 주요 고유성분(eigenvalue)는 PC1 54.0%, PC2 14.2%, PC3 12.3%로 전체 분산의 88.3%를 설명할 수 있었다. PC1은 Ca, Mg, Na, K, Cl, SO₄, DOC가 주요한 영향 인자였으며 PC2는 HCO₃, NO₃, DO에 영향 받음을 확인하였다. 계층적 군집분석 결과, 연구지역 지하수는 Na-Cl 유형의 C-3 관정을 제외하고는 크게 두 그룹으로 구분되어 졌다. 다변량통계분석의 결과에서도 수리지화학, 동위원소, 용존유기물 등의 특성에서 나타나는 것과 유사한 연구지역의 수질특성을 확인할 수 있었다. 연구지역은 차시기 동안 수질변화는 일부 관정을 제외하고는 유의할 만한 수준으로 관찰되지는 않았고, 지하수 사용에 따른 지하수위 회복도 빠르게 진행되고 있는 것으로 나타났다.

• Journal of Bio-Environment Control
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• v.31 no.2
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• pp.79-89
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• 2022

With the wide use of greenhouses, the working hours have been increasing inside the greenhouse for workers. In the closed ventilated greenhouse, the internal environment has less affected to external weather during making a suitable temperature for crop growth. Greenhouse workers are exposed to organic dust including soil dust, pollen, pesticide residues, microorganisms during tillage process, soil grading, fertilizing, and harvesting operations. Therefore, the health status and working environment exposed to workers should be considered inside the greenhouse. It is necessary to secure basic data on particulate matter (PM) concentrations in order to set up dust reduction and health safety plans. To understand the PM concentration of working environment in greenhouse, the PM concnentrations were monitored in the cut-rose and Hallabong greenhouses in terms of PM size, working type, and working period. Compare to no-work (move) period, a significant increase in PM concentration was found during tillage operation in Hallabong greenhouse by 4.94 times on TSP (total suspended particle), 2.71 times on PM-10 (particle size of 10 ㎛ or larger), and 1.53 times on PM-2.5, respectively. During pruning operation in cut-rose greenhouse, TSP concentration was 7.4 times higher and PM-10 concentration was 3.2 times higher than during no-work period. As a result of analysis of PM contribution ratio by particle sizes, it was shown that PM-10 constitute the largest percentage. There was a significant difference in the PM concentration between work and no-work periods, and the concentration of PM during work was significant higher (p < 0.001). It was found that workers were generally exposed to a high level of dust concentration from 2.5 ㎛ to 35.15 ㎛ during tillage operation.