• Journal of the Society of Disaster Information
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• v.8 no.2
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• pp.138-147
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• 2012

In this paper, the various parametric study of collisions between a offshore wind tower and vessels were performed to estimate the ultimate behaviors of the bucket foundation and the tower. Additionally, the stability of the foundation and the energy dissipation capacities of the tower were analyzed. The results shows that the collision energy of the vessel was mainly dissipated by the plastic deformation energy of the tower and the foundation system shown enough bearing capacity against to this severe loading condition.

• Journal of the Society of Disaster Information
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• v.13 no.3
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• pp.332-339
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• 2017

In the case of aged structures, the information of the structure is often lost after the completion of construction, and there is a great difficulty in predicting the durability life of the structure due to the lack of information on concrete formulations. In this study, the durability of concrete specimens was evaluated by various field and indoor test methods based on the core specimens collected from the field, and the durability life of the concrete structures was predicted by using the FEM analysis technique.As a result, the neutralization rate coefficient was $5.38E-6(cm^2/day)$ and the rate of progress was low. And the possibility of complex deterioration due to carbonation and salting was found to be very low.

• Proceedings of the Korea Water Resources Association Conference
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• 2019.05a
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• pp.156-156
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• 2019

In this study, a gated recurrent unit (GRU) network is constructed based on a deep neural network (DNN) with the aim of restoring the missing daily flow data in river basins. Lai Chau hydrological station is located upstream of the Da river basin (Vietnam) is selected as the target station for this study. Input data of the model are data on observed daily flow for 24 years from 1961 to 1984 (before Hoa Binh dam was built) at 5 hydrological stations, in which 4 gauge stations in the basin downstream and restoring - target station (Lai Chau). The total available data is divided into sections for different purposes. The data set of 23 years (1961-1983) was employed for training and validation purposes, with corresponding rates of 80% for training and 20% for validation respectively. Another data set of one year (1984) was used for the testing purpose to objectively verify the performance and accuracy of the model. Though only a modest amount of input data is required and furthermore the Lai Chau hydrological station is located upstream of the Da River, the calculated results based on the suggested model are in satisfactory agreement with observed data, the Nash - Sutcliffe efficiency (NSE) is higher than 95%. The finding of this study illustrated the outstanding performance of the GRU network model in recovering the missing flow data at Lai Chau station. As a result, DNN models, as well as GRU network models, have great potential for application within the field of hydrology and hydraulics.

• Proceedings of the Korea Water Resources Association Conference
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• 2015.05a
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• pp.214-214
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• 2015

Rainfall induced landslides is one of the most devastating natural disasters acting on mountainous areas. In Korea, landslide damage areas increase significantly from 1990s to 2000s due to the increase of both rainfall intensity and rainy days in addition with haphazard land development. This study was carried out based on the application of TRIGRS unsaturated (Transient Rainfall Infiltration and Grid-based Regional Slope stability analysis), a Fortran coded, physically based, and numerical model that can predict landslides for areas where are prone to shallow precipitation. Using TRIGRS combining with the geographic information system (GIS) framework, the landslide incident happened on 27th, July 2011 in Mt. Umyeon in Seoul was modeled. The predicted results which were raster maps showed values of the factors of safety on every pixel at different time steps show a strong agreement with to the observed actual landslide scars in both time and locations. Although some limitations of the program are still needed to be further improved, some soil data as well as landslide information are lack; TRIGRS is proved to be a powerful tool for shallow landslide susceptibility zonation especially in great areas where the input geotechnical and hydraulic data for simulation is not fully available.

• The Journal of the Convergence on Culture Technology
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• v.5 no.4
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• pp.349-354
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• 2019

Workers who die from falls at construction sites account for about 40 percent of the total number of deaths and are the main forms of accidents. In addition, as the nation's population structure is gradually aging, the rate of aging is increasing at construction sites, and the number of crashes due to decreased physical function and cognitive ability is increasing. Accordingly, we conducted a survey on the factors affecting the fall of older workers and would like to present more fundamental measures to prevent falls, focusing on older workers who are the victims of the disaster.

• Proceedings of the Korea Water Resources Association Conference
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• 2018.05a
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• pp.147-147
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• 2018

In this article, we use an open source software library: TensorFlow, developed for the purposes of conducting very complex machine learning and deep neural network applications. However, the system is general enough to be applicable in a wide variety of other domains as well. The proposed model based on a deep neural network model, LSTM (Long Short-Term Memory) to predict the river water level at Okcheon Station of the Guem River without utilization of rainfall - forecast information. For LSTM modeling, the input data is hourly water level data for 15 years from 2002 to 2016 at 4 stations includes 3 upstream stations (Sutong, Hotan, and Songcheon) and the forecasting-target station (Okcheon). The data are subdivided into three purposes: a training data set, a testing data set and a validation data set. The model was formulated to predict Okcheon Station water level for many cases from 3 hours to 12 hours of lead time. Although the model does not require many input data such as climate, geography, land-use for rainfall-runoff simulation, the prediction is very stable and reliable up to 9 hours of lead time with the Nash - Sutcliffe efficiency (NSE) is higher than 0.90 and the root mean square error (RMSE) is lower than 12cm. The result indicated that the method is able to produce the river water level time series and be applicable to the practical flood forecasting instead of hydrologic modeling approaches.

• Korean Journal of Remote Sensing
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• v.35 no.1
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• pp.1-13
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• 2019

The advent of a fourth industrial revolution, built on advances in digital technology, has coincided with studies using various unmanned aerial vehicles (UAVs) being performed worldwide. However, the accuracy of different sensors and their suitability for particular research studies are factors that need to be carefully evaluated. In this study, we evaluated UAV photogrammetry using smart technology. To assess the performance of digital photogrammetry, the accuracy of common procedures for generating orthomosaic images and digital surface models (DSMs) using terrestrial laser scanning (TLS) techniques was measured. Two different type of non-surveying camera(Smartphone camera, fisheye camera) were attached to UAV platform. For fisheye camera, lens distortion was corrected by considering characteristics of lens. Accuracy of orthoimage and DSM generated were comparatively analyzed using aerial and TLS data. Accuracy comparison analysis proceeded as follows. First, we used Ortho mosaic image to compare the check point with a certain area. In addition, vertical errors of camera DSM were compared and analyzed based on TLS. In this study, we propose and evaluate the feasibility of UAV photogrammetry which can acquire 3 - D spatial information at low cost in a construction site.

• Structural Engineering and Mechanics
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• v.82 no.6
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• pp.771-783
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• 2022

Structural damage identification (SDI) methods have been proposed to monitor the safety of structures. However, the traditional SDI methods using modal parameters, such as natural frequencies and mode shapes, are not sensitive enough to structural damage. To tackle this problem, this paper proposes a new SDI method based on transmissibility assurance criterion (TAC) and weighted Schatten-p norm regularization. Firstly, the transmissibility function (TF) has been proved a useful damage index, which can effectively detect structural damage under unknown excitations. Inspired by the modal assurance criterion (MAC), TF and MAC are combined to construct a new damage index, so called as TAC, which is introduced into the objective function together with modal parameters. In addition, the weighted Schatten-p norm regularization method is adopted to improve the ill-posedness of the SDI inverse problem. To evaluate the effectiveness of the proposed method, some numerical simulations and experimental studies in laboratory are carried out. The results show that the proposed method has a high SDI accuracy, especially for weak damages of structures, it can precisely achieve damage locations and quantifications with a good robustness.

• International conference on construction engineering and project management
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• 2020.12a
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• pp.505-509
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• 2020

Twitter is a useful medium to grasp various damage situations that have occurred in society. However, it is a laborious task to spot damage-related topics according to time in the environment where information is constantly produced. This paper proposes a methodology of constructing a knowledge structure by combining the BERT-based classifier and the community detection techniques to discover the topics underlain in the damage information. The methodology consists of two steps. In the first step, the tweets are classified into the classes that are related to human damage, infrastructure damage, and industrial activity damage by a BERT-based transfer learning approach. In the second step, networks of the words that appear in the damage-related tweets are constructed based on the co-occurrence matrix. The derived networks are partitioned by maximizing the modularity to reveal the hidden topics. Five keywords with high values of degree centrality are selected to interpret the topics. The proposed methodology is validated with the Hurricane Harvey test data.

• Proceedings of the Korean Society of Disaster Information Conference
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• 2022.10a
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• pp.209-210
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• 2022

사회적 문제로 떠오르고 있는 건설산업에서 늘 만연한 산업재해 발생을 막기 위해서는 다양한 이해관계자들의 노력을 투자해야 한다. 특히, 이해 관계자들 사이에서 주문자는 프로젝트의 의사 결정 구조의 최상위에 있다. 따라서 주문자의 안전과 건강에 대한 인식은 전체 건설 현장의 안전을 확보하는 과정에 직접적인 영향을 미친다. 이러한 관점에서 본 연구는 최근에 도입된 고객에 대한 의무적인 안전 및 건강 조치에 관한 각 이해 관계자의 인식 차이를 확인하는 것을 목표로 한다. 또한 한국적 맥락에서 구체적인 이행 계획을 제시한다. 분석에 사용된 데이터는 주문자, 안전 관리자, 현장 관리자와 같은 이해 관계자를 대상으로 한 설문 조사를 통해 수집되었으며, 수집된 데이터는 분산 분석과 같은 다변량 분석 방법을 사용하여 정량적으로 검토되었다. 분석 결과, 소유자에 대한 안전 및 건강 의무의 도입이 필요한 것으로 판명되었으며, 안전 및 건강 전문가를 행동 계획으로 지정하고 운영하는 것이 합리적이라고 간주되었다. 저자들은 이번 연구 결과가 한국의 관련 규제 개정을 위한 기본 자료로 활용될 수 있을 것으로 기대하고 있다. 또한, 추가 연구로서, 규제 개선 후 효과에 대한 검토는 도메인에 크게 기여할 것이다.