• 국제학술발표논문집
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• The 9th International Conference on Construction Engineering and Project Management
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• pp.353-360
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• 2022

Due to the dense and complicated working environment, the construction industry is susceptible to many accidents. Worker's fall is a severe problem at the construction site, including falling into holes or openings because of the inadequate coverings as per the safety rules. During the construction or demolition of a building, openings and holes are formed in the floors and roofs. Many workers neglect to cover openings for ease of work while being aware of the risks of holes, openings, and gaps at heights. However, there are safety rules for worker safety; the holes and openings must be covered to prevent falls. The safety inspector typically examines it by visiting the construction site, which is time-consuming and requires safety manager efforts. Therefore, this study presented a worker-driven approach (the worker is involved in the reporting process) to facilitate safety managers by developing integrated computer vision and inertia sensors-based mobile applications to identify openings. The TensorFlow framework is used to design Convolutional Neural Network (CNN); the designed CNN is trained on a custom dataset for binary class openings and covered and deployed on an android smartphone. When an application captures an image, the device also extracts the accelerometer values to determine the inclination in parallel with the classification task of the device to predict the final output as floor (openings/ covered), wall (openings/covered), and roof (openings / covered). The proposed worker-driven approach will be extended with other case scenarios at the construction site.

• 한국건축시공학회:학술대회논문집
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• 한국건축시공학회 2022년도 가을 학술논문 발표대회
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• pp.243-244
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• 2022

In this paper, experiments were conducted on signal amplification of polymer capsules for application to Ground Penetrating Radar so as to enable real-time monitoring of polymer capsules inside concrete using the Morphology Dependent Resonance phenomenon. A TEM CELL and a vector network analyzer were used to analyze the difference in resonance frequency depending on the material of the sphere and the presence or absence of fracture. In order to manufacture a capsule of a size that can be measured using millimeter waves used in GPR, we manufactured a capsule with a 3D printer and analyzed the effects of the presence or absence of coating and the size of the capsule on the resonance frequency. Resonant frequency or signal amplification is more affected by diameter than coating. The capsule showing the highest amplification is the resin-coated 50 mm diameter capsule with a 316-fold increase and the lowest capsule is the uncoated 10 mm diameter capsule with a signal amplification of 11.9 times. These results demonstrate the potential of GPR to measure the position and state of self-healing capsules, which are small-sized polymers, in real time using millimeter waves.

• Journal of Biosystems Engineering
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• 제21권3호
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• pp.325-335
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• 1996

표고의 외관 특징들은 표고의 재배 시 생육상태의 정량적 측정을 위해서, 표고의 건조 시 건조 성능을 나타내는 정량적 지표로서, 그리고 건표고의 품질을 판정하는 요인으로서 중요한 역할을 한다. 본 논문에서는 컴퓨터 시각시스템 및 신경회로망 기술을 적용하여 표고의 갓 및 내피에 고루 분포되어 있는 외관특징을 정량적으로 추출하는 알고리즘을 개발하였다. 기존의 영상 처리 과정에서 유도되는 경험적 판정규칙 또는 명확한 수치적 판정조건에 의한 등급판정은 입력데이타의 결핍 또는 애매모호성에 따른 오차가 발생하기 쉽다. 신경회로망을 이용한 영상인식 기능을 도입함으로써 다양하고 애매모호한 표고의 외관 영상특징들을 효율적으로 처리하여 기존 영상처리 알고리즘에서 발생하는 오차를 개선하였다. 본 논문에서 제안하는 알고리즘은 표고의 갓과 내피면의 인식 및 특징 분할, 꼭지부의 검출, 제거 및 재생 등을 포함한다. 제안한 알고리즘에 의거하여 건표고의 등급판정에 주요한 품질인자들을 추출하고 정량화 하였다. 그리고 알고리즘의 개발은 흑백의 다치입력영상을 이용하여 수행하였다.

• 천문학회보
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• 제45권1호
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• pp.46.3-47
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• 2020

Variations in the brightness of asteroids are caused by their spins, irregular shapes and companions. Thus, in principle, the spin state and shape model of a single object or, a combined model of spins, shapes and mutual orbit of a multiple components can be constructed from the analysis of light curves obtained from the time-series photometry. Using ground- and space-based facilities, a number of time-series photometric observations of asteroids have been conducted to find the possible causes of their light variations. Nonetheless, only about 2% of the known asteroids have been confirmed for their rotation periods. Therefore, a follow-on systematic photometric survey of asteroids is essential. We started an asteroid light curve survey for this purpose using Korea Microlensing Telescope Network (KMTNet) during 199 nights between the second half of 2019 and the first half of 2020. We monitored within a 2° × 14° region of the sky per each night with 25 min cadences. In order to observe as many asteroids as possible with a single exposure, we mostly focus on the ecliptic plane. In our survey, 25,925 asteroids were observed and about 8,000 of them were confirmed for their rotation periods. In addition, using KMTNet's 24-hour continuous monitoring, we collected many composite light curves of slow rotating asteroids that were rarely obtained with previous observations. In this presentation, we will introduce the typical light curves of asteroids obtained from our survey and present a statistical analysis of spin states and shapes of the asteroids from this study.

• Smart Structures and Systems
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• 제32권1호
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• pp.23-35
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• 2023

Steel bolted joint is an important part of steel structure, and its damage directly affects the bearing capacity and durability of steel structure. Currently, the existing research mainly focuses on the identification of corroded bolts and corroded bolts respectively, and there are few studies on multiple states. A detection framework of corroded and loosened bolts is proposed in this study, and the innovations can be summarized as follows: (i) Vision Transformer (ViT) is introduced to replace the third and fourth C3 module of you-only-look-once version 5s (YOLOv5s) algorithm, which increases the attention weights of feature channels and the feature extraction capability. (ii) Three states of the steel bolts are considered, including corroded bolt, bolt missing and clean bolt. (iii) Line segment detector (LSD) is introduced for bolt rotation angle calculation, which realizes bolt looseness detection. The improved YOLOv5s model was validated on the dataset, and the mean average precision (mAP) was increased from 0.902 to 0.952. In terms of a lab-scale joint, the performance of the LSD algorithm and the Hough transform was compared from different perspective angles. The error value of bolt loosening angle of the LSD algorithm is controlled within 1.09%, less than 8.91% of the Hough transform. Furthermore, the proposed framework was applied to fullscale joints of a steel bridge in China. Synthetic images of loosened bolts were successfully identified and the multiple states were well detected. Therefore, the proposed framework can be alternative of monitoring steel bolted joints for management department.

• 대한토목학회논문집
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• 제29권5D호
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• pp.645-654
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• 2009

본 연구에서는 정보화시대를 선도하는 U-City 추진이나 U-City 관련기술의 연구개발도 중요하지만, 오랜 역사적 경험과 지속적 연구로 성숙되어진 공간계획기법들과 연계되는 U-City의 추진이 필요하다는 시점에서 연구를 시작하였다. 연구방법으로서, 최근의 신도시계획에서 나타나는 각종 공간계획지표의 변화특성, 특히 안전이나 방범에 대한 계획지표의 특성을 분석하였으며, 이어서 안전한 도시공간 형성을 위한 유비쿼터스 기법과 전통적인 안전도시구상을 위한 방어공간이론, 최근의 환경설계를 통한 범죄예방(CPTED) 등과 비교분석하였다. 결과, 각각의 계획적 지표들이 계획단계부터 통합되어지지 않고 추진되어지고 있다는 것을 파악할 수 있었으며, 각각의 장단점을 보완, 연계하려는 시도가 부족하다는 것을 알 수 있었다. 특히, 본 연구의 주된 관심사였던 범죄예방를 위한 공간계획의 시점에서 보면, 건축적인 접근과 함께 비건축적인 접근이 상호보완적으로 추진되어야 함에도 불구하고, 기계적인 감시 등 비건축적 접근에 치중하는 경향을 파악할 수 있었다. 즉, CCTV 등의 활용이 시대적 요구라고 할지라도 물리적 공간계획의 효과를 상승시키는 방향에서 고려가 되어야 할 것이나 이에 대한 배려는 미약한 실정이었다.

• 한국수자원학회:학술대회논문집
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• 한국수자원학회 2022년도 학술발표회
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• pp.313-313
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• 2022

일반적으로 가뭄은 특정지역에서 평균 이하의 강수량이 발생되는 현상으로, 강수량이 감소되면 토양수분, 하천수 수위, 저수지 수위, 지하수위 등이 순차적으로 감소한다. 수문학적 가뭄은 기상학적 가뭄 및 농업 가뭄에 비해 늦게 발현되는데, 이는 강수량의 부족이 토양수분, 하천수량, 지하수 및 저수지 수위 등과 같은 수문학적 시스템에 전이되는 시간이 소요되기 때문이다. 따라서, 가뭄 피해를 경감하기 위해 지하수위 변동성을 이용하여 지하수 함양량을 추정함으로써 효율적인 수자원 관리의 필요성이 증대되고 있다. 지하수위는 농촌 지하수 개발, 가뭄 및 홍수 예측 등 다양한 분야에 활용되며, 강수량에 의한 변화가 지표수에 비해 느리게 나타나고 토양을 통과하는 특성으로 인해 단기 및 장기간의 변화 경향이 나타난다. 미국 지질조사국 (United States Geological Survey)에서는 지하수위를 월 단위로 보통 이하 (Below-normal), 보통 (Normal), 보통 이상 (Above-normal) 3단계로 구분하여 분포도를 작성하고 전체 관측기간 중 25% 이상에서 보통 이하 (Below-normal)로 나타나면 가뭄으로 판단한다. 우리나라의 경우 지형, 유역을 고려한 지하수 수위 및 수질 현황과 변동성을 파악하기 위하여 전국 지하수위 관측망 688개소를 설치하고 운영 중에 있다. 또한, 농촌진흥청에서는 전국 농업기상대와 연계하여 토양수분관측망 (soil moisture monitoring network)을 구축하였으며, 표토 10 cm에 토양수분센서를 전국 168 지점에 설치하여 운영하고 있다. 본 연구에서는 강수량을 기반으로 산정한 표준강수지수 (Standardized Precipitation Index, SPI)와 지하수위를 기반으로 산정한 표준지하수위지수 (Standardized Groundwater Level Index, SGI), 토양수분관측망의 토양수분의 상관 분석을 수행하고자 한다. 밭작물 가뭄의 중요 요소인 토양수분 함량은 강수에 즉각적으로 반응하는 반면 지표수 및 지하수는 상대적으로 장기간의 강수에 영향을 받기 때문에, 본 연구의 결과는 향후 밭작물 지역의 가뭄 취약성을 관리하는 지표로 활용이 가능할 것으로 사료된다.

• 국제학술발표논문집
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• The 9th International Conference on Construction Engineering and Project Management
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• pp.792-799
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• 2022

Building Information Modeling (BIM) technology is a key component of modern construction engineering and project management workflows. As-is BIM models that represent the spatial reality of a project site can offer crucial information to stakeholders for construction progress monitoring, error checking, and building maintenance purposes. Geometric methods for automatically converting raw scan data into BIM models (Scan-to-BIM) often fail to make use of higher-level semantic information in the data. Whereas, semantic segmentation methods only output labels at the point level without creating object level models that is necessary for BIM. To address these issues, this research proposes a hybrid semantic-geometric approach for clutter-resistant floorplan generation from laser-scanned building point clouds. The input point clouds are first pre-processed by normalizing the coordinate system and removing outliers. Then, a semantic segmentation network based on PointNet++ is used to label each point as ceiling, floor, wall, door, stair, and clutter. The clutter points are removed whereas the wall, door, and stair points are used for 2D floorplan generation. A region-growing segmentation algorithm paired with geometric reasoning rules is applied to group the points together into individual building elements. Finally, a 2-fold Random Sample Consensus (RANSAC) algorithm is applied to parameterize the building elements into 2D lines which are used to create the output floorplan. The proposed method is evaluated using the metrics of precision, recall, Intersection-over-Union (IOU), Betti error, and warping error.

• 국제학술발표논문집
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• The 8th International Conference on Construction Engineering and Project Management
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• pp.463-481
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• 2020

Cutter suction dredgers (CSDs) are widely used in various dredging constructions such as channel excavation, wharf construction, and reef construction. During a CSD construction, the main operation is to control the swing speed of cutter to keep the slurry concentration in a proper range. However, the slurry concentration cannot be monitored in real-time, i.e., there is a "time-lag effect" in the log of slurry concentration, making it difficult for operators to make the optimal decision on controlling. Concerning this issue, a solution scheme that using real-time monitored indicators to predict current slurry concentration is proposed in this research. The characteristics of the CSD monitoring data are first studied, and a set of preprocessing methods are presented. Then we put forward the concept of "index class" to select the important indices. Finally, an ensemble learning algorithm is set up to fit the relationship between the slurry concentration and the indices of the index classes. In the experiment, log data over seven days of a practical dredging construction is collected. For comparison, the Deep Neural Network (DNN), Long Short Time Memory (LSTM), Support Vector Machine (SVM), Random Forest (RF), Gradient Boosting Decision Tree (GBDT), and the Bayesian Ridge algorithm are tried. The results show that our method has the best performance with an R² of 0.886 and a mean square error (MSE) of 5.538. This research provides an effective way for real-time predicting the slurry concentration of CSDs and can help to improve the stationarity and production efficiency of dredging construction.

• Smart Structures and Systems
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• 제32권5호
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• pp.319-338
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• 2023

The study presents a new hybrid data-driven method by combining radial basis functions neural networks (RBF-NN) with the Jaya algorithm (JA) to provide effective structural health monitoring of arch dams. The novelty of this approach lies in that only one user-defined parameter is required and thus can increase its effectiveness and efficiency, as compared to other machine learning techniques that often require processing a large amount of training and testing model parameters and hyper-parameters, with high time-consuming. This approach seeks rapid damage detection in arch dams under dynamic conditions, to prevent potential disasters, by utilizing the RBF-NNN to seamlessly integrate the dynamic elastic modulus (DEM) and modal parameters (such as natural frequency and mode shape) as damage indicators. To determine the dynamic characteristics of the arch dam, the JA sequentially optimizes an objective function rooted in vibration-based data sets. Two case studies of hyperbolic concrete arch dams were carefully designed using finite element simulation to demonstrate the effectiveness of the RBF-NN model, in conjunction with the Jaya algorithm. The testing results demonstrated that the proposed methods could exhibit significant computational time-savings, while effectively detecting damage in arch dam structures with complex nonlinearities. Furthermore, despite training data contaminated with a high level of noise, the RBF-NN and JA fusion remained the robustness, with high accuracy.